Search Results for author: Feiping Nie

Found 64 papers, 6 papers with code

Simple Multigraph Convolution Networks

1 code implementation8 Mar 2024 Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu, Feiping Nie

Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators.

Embedded Multi-label Feature Selection via Orthogonal Regression

no code implementations1 Mar 2024 Xueyuan Xu, Fulin Wei, Tianyuan Jia, Li Zhuo, Feiping Nie, Xia Wu

Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data.

feature selection Model Optimization +2

Multi-class Support Vector Machine with Maximizing Minimum Margin

1 code implementation11 Dec 2023 Feiping Nie, Zhezheng Hao, Rong Wang

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks.

Binary Classification

A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization

no code implementations26 Nov 2023 Feiping Nie, Jitao Lu, Danyang Wu, Rong Wang, Xuelong Li

To address the problems, we propose a novel N-Cut solver designed based on the famous coordinate descent method.


NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks

no code implementations2 Oct 2023 Xinjie Shen, Danyang Wu, Jitao Lu, Junjie Liang, Jin Xu, Feiping Nie

Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.

Graph Learning Link Prediction +2

AGFormer: Efficient Graph Representation with Anchor-Graph Transformer

no code implementations12 May 2023 Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, Feiping Nie

To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.

On the Global Solution of Soft k-Means

no code implementations7 Dec 2022 Feiping Nie, Hong Chen, Rong Wang, Xuelong Li

This paper presents an algorithm to solve the Soft k-Means problem globally.


Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework

no code implementations3 Nov 2022 Liangchen Liu, Qiuhong Ke, Chaojie Li, Feiping Nie, Yingying Zhu

In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).


A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

no code implementations11 Apr 2022 Junyun Cui, Xiaoyu Shen, Feiping Nie, Zheng Wang, Jinglong Wang, Yulong Chen

In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes; (2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.

Compactness Score: A Fast Filter Method for Unsupervised Feature Selection

no code implementations31 Jan 2022 Peican Zhu, Xin Hou, Keke Tang, Zhen Wang, Feiping Nie

For feature engineering, feature selection seems to be an important research content in which is anticipated to select "excellent" features from candidate ones.

Decision Making Dimensionality Reduction +2

Adaptive neighborhood Metric learning

no code implementations20 Jan 2022 Kun Song, Junwei Han, Gong Cheng, Jiwen Lu, Feiping Nie

In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining.

Metric Learning

New Tight Relaxations of Rank Minimization for Multi-Task Learning

no code implementations9 Dec 2021 Wei Chang, Feiping Nie, Rong Wang, Xuelong Li

Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace.

Multi-Task Learning

Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

no code implementations6 Jan 2021 Rong Wang, Yihang Lu, Qianrong Zhang, Feiping Nie, Zhen Wang, Xuelong Li

To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages.

Anomaly Detection Ensemble Learning

Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection

no code implementations29 Dec 2020 Zhengxin Li, Feiping Nie, Jintang Bian, Xuelong Li

However, real-world data contain a large number of noise samples and features, making the similarity matrix constructed by original data cannot be completely reliable.

feature selection

Learning Feature Sparse Principal Subspace

1 code implementation NeurIPS 2020 Lai Tian, Feiping Nie, Rong Wang, Xuelong Li

This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.

Dimensionality Reduction feature selection

Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut

1 code implementation NeurIPS 2020 Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li

In particular, over 15x and 7x speed-up can be obtained with respect to $k$-means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].


Self-Weighted Robust LDA for Multiclass Classification with Edge Classes

no code implementations24 Sep 2020 Caixia Yan, Xiaojun Chang, Minnan Luo, Qinghua Zheng, Xiaoqin Zhang, Zhihui Li, Feiping Nie

In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.

Classification Computational Efficiency +2

NP-PROV: Neural Processes with Position-Relevant-Only Variances

no code implementations15 Jun 2020 Xuesong Wang, Lina Yao, Xianzhi Wang, Feiping Nie

Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.


Agglomerative Neural Networks for Multi-view Clustering

no code implementations12 May 2020 Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Feiping Nie

Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.


Curriculum Audiovisual Learning

no code implementations26 Jan 2020 Di Hu, Zheng Wang, Haoyi Xiong, Dong Wang, Feiping Nie, Dejing Dou

Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data.


Supervised feature selection with orthogonal regression and feature weighting

no code implementations9 Oct 2019 Xia Wu, Xueyuan Xu, Jianhong Liu, Hailing Wang, Bin Hu, Feiping Nie

Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data.

feature selection regression

Longitudinal Enrichment of Imaging Biomarker Representations for Improved Alzheimer's Disease Diagnosis

no code implementations25 Sep 2019 Saad Elbeleidy, Lyujian Lu, L. Zoe Baker, Hua Wang, Feiping Nie

Longitudinal data is often available inconsistently across individuals resulting in ignoring of additionally available data.

Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

no code implementations19 Aug 2019 Jinglin Xu, Junwei Han, Mingliang Xu, Feiping Nie, Xuelong. Li

Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.


An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms

no code implementations2 Jul 2019 Feiping Nie, Zhanxuan Hu, Xiaoqian Wang, Rong Wang, Xuelong. Li, Heng Huang

This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.

BIG-bench Machine Learning Clustering +2

Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms

no code implementations29 Jun 2019 Feiping Nie, Hua Wang, Zheng Wang, Heng Huang

In this paper, we propose a novel robust linear discriminant analysis method based on the L1, 2-norm ratio minimization.

Intrinsic Weight Learning Approach for Multi-view Clustering

no code implementations21 Jun 2019 Feiping Nie, Jing Li, Xuelong. Li

Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering.


Learning Feature Sparse Principal Components

no code implementations23 Apr 2019 Lai Tian, Feiping Nie, Xuelong. Li

This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.

feature selection

Listen to the Image

no code implementations CVPR 2019 Di Hu, Dong Wang, Xuelong. Li, Feiping Nie, Qi. Wang

different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.


Feature Learning Viewpoint of AdaBoost and a New Algorithm

no code implementations8 Apr 2019 Fei Wang, Zhongheng Li, Fang He, Rong Wang, Weizhong Yu, Feiping Nie

We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.

Deep LDA Hashing

no code implementations8 Oct 2018 Di Hu, Feiping Nie, Xuelong. Li

The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does.

Dense Multimodal Fusion for Hierarchically Joint Representation

no code implementations8 Oct 2018 Di Hu, Feiping Nie, Xuelong. Li

Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities.

Cross-Modal Retrieval Retrieval +2

Low Rank Regularization: A Review

no code implementations14 Aug 2018 Zhanxuan Hu, Feiping Nie, Rong Wang, Xuelong Li

Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version.

BIG-bench Machine Learning Image Denoising

Deep Multimodal Clustering for Unsupervised Audiovisual Learning

1 code implementation CVPR 2019 Di Hu, Feiping Nie, Xuelong. Li

And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion.


Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances

no code implementations CVPR 2018 Kai Liu, Hua Wang, Feiping Nie, Hao Zhang

To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.

Representation Learning

Ranking with Adaptive Neighbors

no code implementations14 Mar 2018 Muge Li, Liangyue Li, Feiping Nie

Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix.

Learning A Structured Optimal Bipartite Graph for Co-Clustering

no code implementations NeurIPS 2017 Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang

In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.


A Self-Balanced Min-Cut Algorithm for Image Clustering

no code implementations ICCV 2017 Xiaojun Chen, Joshua Zhexue Haung, Feiping Nie, Renjie Chen, Qingyao Wu

In the new method, a self-balanced min-cut model is proposed in which the Exclusive Lasso is implicitly introduced as a balance regularizer in order to produce balanced partition.

Clustering Content-Based Image Retrieval +2

Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction

no code implementations9 Sep 2017 Yanwei Pang, Bo Zhou, Feiping Nie

It is interesting that the optimal regularization parameter is adaptive to the neighbors in low-dimensional space and has intuitive meaning.

Supervised dimensionality reduction

Deep Binary Reconstruction for Cross-modal Hashing

1 code implementation17 Aug 2017 Xuelong. Li, Di Hu, Feiping Nie

Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion.

Cross-Modal Retrieval Retrieval

Local Shrunk Discriminant Analysis (LSDA)

no code implementations3 May 2017 Zan Gao, Guotai Zhang, Feiping Nie, Hua Zhang

Principal component analysis (PCA) is a traditional technique for unsupervised dimensionality reduction, which is often employed to seek a projection to best represent the data in a least-squares sense, but if the original data is nonlinear structure, the performance of PCA will quickly drop.

Supervised dimensionality reduction

From Photo Streams to Evolving Situations

no code implementations20 Feb 2017 Mengfan Tang, Feiping Nie, Siripen Pongpaichet, Ramesh Jain

Photos are becoming spontaneous, objective, and universal sources of information.

A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification

no code implementations14 Oct 2016 Shuai Zheng, Feiping Nie, Chris Ding, Heng Huang

In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.

Classification General Classification +2

Uncovering Locally Discriminative Structure for Feature Analysis

no code implementations9 Jul 2016 Sen Wang, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao

We propose a method that utilizes both the manifold structure of data and local discriminant information.

Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking

no code implementations CVPR 2016 Rong Quan, Junwei Han, Dingwen Zhang, Feiping Nie

Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years.

Object Segmentation

Discriminatively Embedded K-Means for Multi-View Clustering

no code implementations CVPR 2016 Jinglin Xu, Junwei Han, Feiping Nie

In real world applications, more and more data, for example, image/video data, are high dimensional and represented by multiple views which describe different perspectives of the data.


Non-Greedy L21-Norm Maximization for Principal Component Analysis

no code implementations28 Mar 2016 Feiping Nie, Heng Huang

In this paper, we propose to maximize the L21-norm based robust PCA objective, which is theoretically connected to the minimization of reconstruction error.

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

no code implementations5 Sep 2015 Wenhao Jiang, Cheng Deng, Wei Liu, Feiping Nie, Fu-Lai Chung, Heng Huang

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.

Domain Adaptation

Unsupervised Feature Analysis with Class Margin Optimization

no code implementations3 Jun 2015 Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng

In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.

Clustering Feature Correlation +1

Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation

no code implementations CVPR 2015 Lianli Gao, Jingkuan Song, Feiping Nie, Yan Yan, Nicu Sebe, Heng Tao Shen

In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.

graph construction Graph Learning

Effective Discriminative Feature Selection with Non-trivial Solutions

no code implementations21 Apr 2015 Hong Tao, Chenping Hou, Feiping Nie, Yuanyuan Jiao, Dongyun Yi

In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization.

Dimensionality Reduction feature selection

A Convex Sparse PCA for Feature Analysis

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang

In addition, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability.

Dimensionality Reduction feature selection +1

A Convex Formulation for Spectral Shrunk Clustering

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou

Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance.

Clustering Dimensionality Reduction

Balanced k-Means and Min-Cut Clustering

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang

Clustering is an effective technique in data mining to generate groups that are the matter of interest.


Compound Rank-k Projections for Bilinear Analysis

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Sen Wang, Yi Yang, Xiaofang Zhou, Chengqi Zhang

In many real-world applications, data are represented by matrices or high-order tensors.

Improved Spectral Clustering via Embedded Label Propagation

no code implementations23 Nov 2014 Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang

Our algorithm is built upon two advancements of the state of the art:1) label propagation, which propagates a node\'s labels to neighboring nodes according to their proximity; and 2) manifold learning, which has been widely used in its capacity to leverage the manifold structure of data points.


Heterogeneous Visual Features Fusion via Sparse Multimodal Machine

no code implementations CVPR 2013 Hua Wang, Feiping Nie, Heng Huang, Chris Ding

We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.

Feature Importance Multi-Label Image Classification +2

Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

no code implementations NeurIPS 2010 Feiping Nie, Heng Huang, Xiao Cai, Chris H. Ding

The ℓ2, 1-norm based loss function is robust to outliers in data points and the ℓ2, 1-norm regularization selects features across all data points with joint sparsity.

feature selection regression

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