Search Results for author: Junbin Gao

Found 105 papers, 29 papers with code

Hierarchical Multi-modal Transformer for Cross-modal Long Document Classification

no code implementations14 Jul 2024 Tengfei Liu, Yongli Hu, Junbin Gao, Yanfeng Sun, BaoCai Yin

In this paper, we propose a novel approach called Hierarchical Multi-modal Transformer (HMT) for cross-modal long document classification.

Document Classification Sentence

Unleash Graph Neural Networks from Heavy Tuning

no code implementations21 May 2024 Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao

Our method: (1) unleashes GNN training from heavy tuning and complex search space design; (2) produces GNN parameters that outperform those obtained through comprehensive grid search; and (3) establishes higher-quality generation for GNNs compared to diffusion frameworks designed for general neural networks.

ST-MambaSync: The Complement of Mamba and Transformers for Spatial-Temporal in Traffic Flow Prediction

no code implementations24 Apr 2024 Zhiqi Shao, Xusheng Yao, Ze Wang, Junbin Gao

This paper introduces ST-MambaSync, an innovative traffic flow prediction model that combines transformer technology with the ST-Mamba block, representing a significant advancement in the field.

Computational Efficiency Management +1

ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction

no code implementations20 Apr 2024 Zhiqi Shao, Michael G. H. Bell, Ze Wang, D. Glenn Geers, Haoning Xi, Junbin Gao

Traffic flow prediction, a critical aspect of intelligent transportation systems, has been increasingly popular in the field of artificial intelligence, driven by the availability of extensive traffic data.

Computational Efficiency Management +1

IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion

no code implementations28 Mar 2024 Jiapu Wang, Zheng Cui, Boyue Wang, Shirui Pan, Junbin Gao, BaoCai Yin, Wen Gao

However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures.

Temporal Knowledge Graph Completion

DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations

1 code implementation28 Jan 2024 Jinlu Wang, Jipeng Guo, Yanfeng Sun, Junbin Gao, Shaofan Wang, Yachao Yang, BaoCai Yin

To obtain a more comprehensive embedding representation of nodes, a novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced.

Attribute Graph Embedding +3

Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks

no code implementations26 Jan 2024 Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao

Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, over-squashing, and heterophily adaption.

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting

no code implementations16 Jan 2024 Lequan Lin, Dai Shi, Andi Han, Junbin Gao

Our method generates the Fourier representation of future time series, transforming the learning process into the spectral domain enriched with spatial information.

Time Series

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges

no code implementations13 Nov 2023 Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao

Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks.

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

no code implementations16 Oct 2023 Andi Han, Dai Shi, Lequan Lin, Junbin Gao

Such a scheme has been found to be intrinsically linked to a physical process known as heat diffusion, where the propagation of GNNs naturally corresponds to the evolution of heat density.

Bregman Graph Neural Network

1 code implementation12 Sep 2023 Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao

Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption.

Bilevel Optimization Graph Neural Network +1

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

no code implementations6 Sep 2023 Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao

To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power.

How Curvature Enhance the Adaptation Power of Framelet GCNs

1 code implementation19 Jul 2023 Dai Shi, Yi Guo, Zhiqi Shao, Junbin Gao

Motivated by the geometric analogy of Ricci curvature in the graph setting, we prove that by inserting the curvature information with different carefully designed transformation function $\zeta$, several known computational issues in GNN such as over-smoothing can be alleviated in our proposed model.

Graph Classification Graph Neural Network

Frameless Graph Knowledge Distillation

1 code implementation13 Jul 2023 Dai Shi, Zhiqi Shao, Yi Guo, Junbin Gao

Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy.

Graph Representation Learning Knowledge Distillation

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

no code implementations5 Jul 2023 Qijie Ding, Jie Yin, Daokun Zhang, Junbin Gao

Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity.

Entity Alignment Knowledge Graphs +1

Variational Counterfactual Prediction under Runtime Domain Corruption

no code implementations23 Jun 2023 Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Junbin Gao, Hongzhi Yin

We term the co-occurrence of domain shift and inaccessible variables runtime domain corruption, which seriously impairs the generalizability of a trained counterfactual predictor.

counterfactual Domain Adaptation +1

Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning

1 code implementation6 Jun 2023 Peggy Tang, Junbin Gao, Lei Zhang, Zhiyong Wang

Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation.

Hallucination reinforcement-learning

Diffusion Models for Time Series Applications: A Survey

no code implementations1 May 2023 Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao

Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research.

Imputation Time Series +1

Graph Contrastive Learning with Implicit Augmentations

1 code implementation7 Nov 2022 Huidong Liang, Xingjian Du, Bilei Zhu, Zejun Ma, Ke Chen, Junbin Gao

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e. g., randomly adding or dropping edges and nodes).

Contrastive Learning Graph Classification +1

A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs

no code implementations20 Oct 2022 Lequan Lin, Junbin Gao

Spectral Graph Convolutional Networks (spectral GCNNs), a powerful tool for analyzing and processing graph data, typically apply frequency filtering via Fourier transform to obtain representations with selective information.

Denoising Link Prediction +1

SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP

1 code implementation18 Oct 2022 Jie Chen, Shouzhen Chen, Mingyuan Bai, Junbin Gao, Junping Zhang, Jian Pu

Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.

Knowledge Distillation Node Classification

Generalized energy and gradient flow via graph framelets

no code implementations8 Oct 2022 Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao

In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow.

Graph Neural Network

Riemannian accelerated gradient methods via extrapolation

no code implementations13 Aug 2022 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

In this paper, we propose a simple acceleration scheme for Riemannian gradient methods by extrapolating iterates on manifolds.

Embedding Graphs on Grassmann Manifold

1 code implementation30 May 2022 Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao

Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.

Graph Embedding Graph Property Prediction +2

A Simple Yet Effective SVD-GCN for Directed Graphs

1 code implementation19 May 2022 Chunya Zou, Andi Han, Lequan Lin, Junbin Gao

In this paper, we propose a simple yet effective graph neural network for directed graphs (digraph) based on the classic Singular Value Decomposition (SVD), named SVD-GCN.

Denoising Graph Neural Network +1

Differentially private Riemannian optimization

no code implementations19 May 2022 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

We introduce a framework of differentially private Riemannian optimization by adding noise to the Riemannian gradient on the tangent space.

Riemannian optimization

OTExtSum: Extractive Text Summarisation with Optimal Transport

1 code implementation Findings (NAACL) 2022 Peggy Tang, Kun Hu, Rui Yan, Lei Zhang, Junbin Gao, Zhiyong Wang

Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions.


Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily

1 code implementation19 Mar 2022 Jie Chen, Shouzhen Chen, Junbin Gao, Zengfeng Huang, Junping Zhang, Jian Pu

Moreover, we propose a simple yet effective Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets by learning the neighbor effect for each node.

Node Classification

Riemannian block SPD coupling manifold and its application to optimal transport

1 code implementation30 Jan 2022 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

In this work, we study the optimal transport (OT) problem between symmetric positive definite (SPD) matrix-valued measures.

Riemannian optimization

Quasi-Framelets: Another Improvement to GraphNeural Networks

no code implementations11 Jan 2022 Mengxi Yang, Xuebin Zheng, Jie Yin, Junbin Gao

This paper aims to provide a novel design of a multiscale framelets convolution for spectral graph neural networks.

Graph Learning Graph Neural Network

Wasserstein Adversarially Regularized Graph Autoencoder

1 code implementation9 Nov 2021 Huidong Liang, Junbin Gao

This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric.

Clustering Link Prediction +1

Graph Denoising with Framelet Regularizer

1 code implementation5 Nov 2021 Bingxin Zhou, Ruikun Li, Xuebin Zheng, Yu Guang Wang, Junbin Gao

As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise.


Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications

no code implementations22 Jul 2021 Mingyuan Bai, S. T. Boris Choy, Junping Zhang, Junbin Gao

In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced.

Clustering Time Series +1

Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures

no code implementations14 Jun 2021 Renlong Jie, Junbin Gao

It is extended from the existing study on differentiable neural architecture search, and we made the backbone architecture transformable rather than fixed during the training process.

Language Modelling Neural Architecture Search +2

A Discussion On the Validity of Manifold Learning

no code implementations3 Jun 2021 Dai Shi, Andi Han, Yi Guo, Junbin Gao

In this work, we investigate the validity of learning results of some widely used DR and ManL methods through the chart mapping function of a manifold.

Dimensionality Reduction speech-recognition +2

On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry

1 code implementation NeurIPS 2021 Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

We build on this to show that the BW metric is a more suitable and robust choice for several Riemannian optimization problems over ill-conditioned SPD matrices.

Riemannian optimization

Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification

no code implementations28 Apr 2021 Jie Chen, Shouzhen Chen, Mingyuan Bai, Jian Pu, Junping Zhang, Junbin Gao

In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.

General Classification Graph Learning +2

Grassmann Graph Embedding

no code implementations ICLR Workshop GTRL 2021 Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao

Geometric deep learning that employs the geometric and topological features of data has attracted increasing attention in deep neural networks.

Dimensionality Reduction Graph Embedding

How Framelets Enhance Graph Neural Networks

1 code implementation13 Feb 2021 Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Lio, Ming Li, Guido Montufar

The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.


CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering

1 code implementation18 Jan 2021 Guangyu Huo, Yong Zhang, Junbin Gao, Boyue Wang, Yongli Hu, BaoCai Yin

In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks.

Clustering Deep Clustering

Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient

no code implementations23 Oct 2020 Andi Han, Junbin Gao

In this paper, we propose a variant of Riemannian stochastic recursive gradient method that can achieve second-order convergence guarantee and escape saddle points using simple perturbation.

Riemannian optimization

Adaptive Hierarchical Hyper-gradient Descent

no code implementations17 Aug 2020 Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran

In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures.


Riemannian stochastic recursive momentum method for non-convex optimization

no code implementations11 Aug 2020 Andi Han, Junbin Gao

We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a near-optimal complexity of $\tilde{\mathcal{O}}(\epsilon^{-3})$ to find $\epsilon$-approximate solution with one sample.

Riemannian optimization

Regularized Flexible Activation Function Combinations for Deep Neural Networks

no code implementations26 Jul 2020 Renlong Jie, Junbin Gao, Andrey Vasnev, Min-ngoc Tran

Based on this, a novel family of flexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs.

Image Compression Philosophy +2

MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning

no code implementations22 Jul 2020 Xuebin Zheng, Bingxin Zhou, Ming Li, Yu Guang Wang, Junbin Gao

In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies.

Graph Classification

Variance reduction for Riemannian non-convex optimization with batch size adaptation

no code implementations3 Jul 2020 Andi Han, Junbin Gao

Variance reduction techniques are popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both Euclidean space and Riemannian manifold.

Riemannian optimization

A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting

no code implementations23 Jan 2020 Zhengkun Li, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Junbin Gao

Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement.

Bayesian Inference Time Series +1

On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods

no code implementations17 Jan 2020 Bingxin Zhou, Xuebin Zheng, Junbin Gao

Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning.

Computational Efficiency Stochastic Optimization

Coupling Matrix Manifolds and Their Applications in Optimal Transport

no code implementations15 Nov 2019 Dai Shi, Junbin Gao, Xia Hong, S. T. Boris Choy, Zhiyong Wang

These geometrical features of CMM have paved the way for developing numerical Riemannian optimization algorithms such as Riemannian gradient descent and Riemannian trust-region algorithms, forming a uniform optimization method for all types of OT problems.

Riemannian optimization

LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering

no code implementations19 Oct 2019 Di Xu, Tianhang Long, Junbin Gao

Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing this data into clusters, i. e. achieve the purpose of dimensional deduction, and meanwhile learning its temporal evolution patterns.



no code implementations25 Sep 2019 Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran

Based on this, we develop two novel flexible activation functions that can be implemented in LSTM cells and auto-encoder layers.

Image Classification Philosophy +2

Tensor-Train Parameterization for Ultra Dimensionality Reduction

no code implementations14 Aug 2019 Mingyuan Bai, S. T. Boris Choy, Xin Song, Junbin Gao

Thus, we propose a tensor-train parameterization for ultra dimensionality reduction (TTPUDR) in which the traditional LPP mapping is tensorized in terms of tensor-trains and the LPP objective is replaced with the Frobenius norm to increase the robustness of the model.

Dimensionality Reduction

Shared Generative Latent Representation Learning for Multi-view Clustering

1 code implementation23 Jul 2019 Ming Yin, Weitian Huang, Junbin Gao

Clustering multi-view data has been a fundamental research topic in the computer vision community.

Clustering Diversity +1

DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

1 code implementation10 Jun 2019 Darren Yates, Md Zahidul Islam, Junbin Gao

Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data-mining and knowledge discovery tools for mobile devices are surprisingly rare.

Cloud Computing Clustering +1

Manifold Optimization Assisted Gaussian Variational Approximation

no code implementations11 Feb 2019 Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach

Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings.

Bayesian Inference

Sparse Least Squares Low Rank Kernel Machines

no code implementations29 Jan 2019 Di Xu, Manjing Fang, Xia Hong, Junbin Gao

A general framework of least squares support vector machine with low rank kernels, referred to as LR-LSSVM, is introduced in this paper.

Computational Efficiency

A Review for Weighted MinHash Algorithms

1 code implementation12 Nov 2018 Wei Wu, Bin Li, Ling Chen, Junbin Gao, Chengqi Zhang

In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).

Data Structures and Algorithms

Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification

no code implementations3 Aug 2018 Lin Wu, Yang Wang, Junbin Gao, Xue Li

Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security.

Metric Learning Video-Based Person Re-Identification

Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

1 code implementation30 Apr 2018 Lin Wu, Yang Wang, Junbin Gao, DaCheng Tao

Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring.

Foveation Person Re-Identification +1

Tensorial Recurrent Neural Networks for Longitudinal Data Analysis

no code implementations1 Aug 2017 Mingyuan Bai, Boyan Zhang, Junbin Gao

In this paper, we propose a new variant of tensorial neural networks which directly take tensorial time series data as inputs.

Time Series Time Series Analysis

What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

no code implementations21 Jul 2017 Lin Wu, Yang Wang, Xue Li, Junbin Gao

To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way.

Person Re-Identification

Vectorial Dimension Reduction for Tensors Based on Bayesian Inference

no code implementations3 Jul 2017 Fujiao Ju, Yanfeng Sun, Junbin Gao, Yongli Hu, Bao-Cai Yin

Under this expression, the projection base of the model is based on the tensor CandeComp/PARAFAC (CP) decomposition and the number of free parameters in the model only grows linearly with the number of modes rather than exponentially.

Bayesian Inference Clustering +1

Low-Rank-Sparse Subspace Representation for Robust Regression

no code implementations CVPR 2017 Yongqiang Zhang, Daming Shi, Junbin Gao, Dansong Cheng

Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications.


Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

no code implementations10 Jun 2017 Lin Wu, Yang Wang, Junbin Gao, Xue Li

To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding.

Metric Learning Person Re-Identification

Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem

no code implementations9 Jun 2017 Yanfei Zhang, Junbin Gao

In the traditional approach to solving this problem, it relies on the probability distribution of the demand.


Localized LRR on Grassmann Manifolds: An Extrinsic View

no code implementations17 May 2017 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin

Subspace data representation has recently become a common practice in many computer vision tasks.


Locality Preserving Projections for Grassmann manifold

no code implementations27 Apr 2017 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, Bao-Cai Yin

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.

Clustering Dimensionality Reduction

Collaborative Low-Rank Subspace Clustering

1 code implementation13 Apr 2017 Stephen Tierney, Yi Guo, Junbin Gao

In this paper we present Collaborative Low-Rank Subspace Clustering.


Efficient Sparse Subspace Clustering by Nearest Neighbour Filtering

1 code implementation13 Apr 2017 Stephen Tierney, Yi Guo, Junbin Gao

Sparse Subspace Clustering (SSC) has been used extensively for subspace identification tasks due to its theoretical guarantees and relative ease of implementation.

Clustering General Classification

Tractable Clustering of Data on the Curve Manifold

1 code implementation13 Apr 2017 Stephen Tierney, Junbin Gao, Yi Guo, Zheng Zhang

However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.


Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications

no code implementations21 Sep 2016 Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu, Bao-Cai Yin

Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets.

General Classification regression

Matrix Variate RBM Model with Gaussian Distributions

no code implementations21 Sep 2016 Simeng Liu, Yanfeng Sun, Yongli Hu, Junbin Gao, Bao-Cai Yin

Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution.

General Classification Image Classification

Low-rank Multi-view Clustering in Third-Order Tensor Space

no code implementations30 Aug 2016 Ming Yin, Junbin Gao, Shengli Xie, Yi Guo

Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.

Clustering Multi-view Subspace Clustering

Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance

no code implementations13 Jun 2016 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin

In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering.

Clustering Human Activity Recognition

Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices

no code implementations27 Jan 2016 Ming Yin, Shengli Xie, Yi Guo, Junbin Gao, Yun Zhang

Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.

Classification General Classification +2

Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds

no code implementations21 Jan 2016 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin

As a significant subspace clustering method, low rank representation (LRR) has attracted great attention in recent years.


Kernelized LRR on Grassmann Manifolds for Subspace Clustering

no code implementations9 Jan 2016 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin

The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework.


Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition

no code implementations7 Jan 2016 Xinglin Piao, Yongli Hu, Yanfeng Sun, Junbin Gao, Bao-Cai Yin

In a sparse representation based recognition scheme, it is critical to learn a desired dictionary, aiming both good representational power and discriminative performance.

Dictionary Learning

Low-Rank Representation over the Manifold of Curves

no code implementations5 Jan 2016 Stephen Tierney, Junbin Gao, Yi Guo, Zhengwu Zhang

However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.

Matrix Variate RBM and Its Applications

no code implementations5 Jan 2016 Guanglei Qi, Yanfeng Sun, Junbin Gao, Yongli Hu, Jinghua Li

In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly model matrix data.

Handwritten Digit Recognition Image Super-Resolution

Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds

no code implementations CVPR 2016 Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie

Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.


Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data

no code implementations2 Jan 2016 Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Zhouchen Lin, Bao-Cai Yin

A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper.


Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation

no code implementations7 Dec 2015 Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu

The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate is proposed.

Matrix Completion

l1-norm Penalized Orthogonal Forward Regression

no code implementations4 Sep 2015 Xia Hong, Sheng Chen, Yi Guo, Junbin Gao

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).


Low Rank Representation on Riemannian Manifold of Square Root Densities

no code implementations18 Aug 2015 Yifan Fu, Junbin Gao, Xia Hong, David Tien

In this paper, we present a novel low rank representation (LRR) algorithm for data lying on the manifold of square root densities.

Clustering General Classification

Segmentation of Subspaces in Sequential Data

1 code implementation16 Apr 2015 Stephen Tierney, Yi Guo, Junbin Gao

We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces.

Clustering Segmentation

Kernelized Low Rank Representation on Grassmann Manifolds

no code implementations8 Apr 2015 Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin

One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to.


Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

no code implementations7 Apr 2015 Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra, Bao-Cai Yin

In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.

Clustering Tensor Decomposition

Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient

no code implementations10 Mar 2015 Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi

Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR).

Clustering Matrix Completion

Relations among Some Low Rank Subspace Recovery Models

no code implementations6 Dec 2014 Hongyang Zhang, Zhouchen Lin, Chao Zhang, Junbin Gao

More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.

Subspace Clustering for Sequential Data

no code implementations CVPR 2014 Stephen Tierney, Junbin Gao, Yi Guo

We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces.


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