Search Results for author: Qiang Cheng

Found 41 papers, 12 papers with code

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

1 code implementation14 Mar 2024 Md Atik Ahamed, Qiang Cheng

Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency.

Computational Efficiency Time Series +1

MambaTab: A Simple Yet Effective Approach for Handling Tabular Data

no code implementations16 Jan 2024 Md Atik Ahamed, Qiang Cheng

Tabular data remains ubiquitous across domains despite growing use of images and texts for machine learning.


Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance

no code implementations6 May 2023 Hua Lan, Jinjie Hu, Zengfu Wang, Qiang Cheng

Motivated by the maneuvering target tracking with sensors such as radar and sonar, this paper considers the joint and recursive estimation of the dynamic state and the time-varying process noise covariance in nonlinear state space models.

Bayesian Inference Variational Inference

RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling

no code implementations10 Jan 2023 Shengyu Zhu, Zehua Yu, Qinghua Guo, Jinshan Ding, Qiang Cheng, Tie Jun Cui

Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes.

Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction

no code implementations30 Sep 2022 Xinxing Wu, Chong Peng, Richard Charnigo, Qiang Cheng

Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients.

Survival Prediction

PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions

1 code implementation25 Aug 2022 Xinxing Wu, Chong Peng, Gregory Jicha, Donna Wilcock, Qiang Cheng

Then, we apply it to study oscillation patterns in untimed genome-wide gene expression from 19 human brain regions of controls and AD patients.

Log-based Sparse Nonnegative Matrix Factorization for Data Representation

no code implementations22 Apr 2022 Chong Peng, Yiqun Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng

Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.

Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization

no code implementations8 Jan 2022 Chong Peng, Yang Liu, Yongyong Chen, Xinxin Wu, Andrew Cheng, Zhao Kang, Chenglizhao Chen, Qiang Cheng

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.

Hyperspectral Image Denoising Image Denoising

Top-$k$ Regularization for Supervised Feature Selection

no code implementations4 Jun 2021 Xinxing Wu, Qiang Cheng

Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of domain problems.

Benchmarking feature selection

Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction

1 code implementation21 Mar 2021 Xinxing Wu, Qiang Cheng

Graph neural networks have been used for a variety of learning tasks, such as link prediction, node classification, and node clustering.

Benchmarking Clustering +4

On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network

no code implementations5 Mar 2021 Wankai Tang, Xiangyu Chen, Ming Zheng Chen, Jun Yan Dai, Yu Han, Shi Jin, Qiang Cheng, Geoffrey Ye Li, Tie Jun Cui

Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.

Information Theory Information Theory

Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band

no code implementations21 Jan 2021 Wankai Tang, Xiangyu Chen, Ming Zheng Chen, Jun Yan Dai, Yu Han, Marco Di Renzo, Shi Jin, Qiang Cheng, Tie Jun Cui

The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.

Kernel Two-Dimensional Ridge Regression for Subspace Clustering

no code implementations3 Nov 2020 Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng

It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data.

Clustering regression +1

Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm

1 code implementation NeurIPS 2021 Xinxing Wu, Qiang Cheng

Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes.

Decision Making Dimensionality Reduction +1

Fractal Autoencoders for Feature Selection

1 code implementation19 Oct 2020 Xinxing Wu, Qiang Cheng

In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE).

feature selection

Structured Graph Learning for Clustering and Semi-supervised Classification

no code implementations31 Aug 2020 Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian

Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.

Classification Clustering +2

Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering

no code implementations19 May 2020 Chong Peng, Zhilu Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng

In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.

Clustering Vocal Bursts Valence Prediction

MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation

no code implementations20 Dec 2019 Wankai Tang, Jun Yan Dai, Ming Zheng Chen, Kai-Kit Wong, Xiao Li, Xinsheng Zhao, Shi Jin, Qiang Cheng, Tie Jun Cui

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface.

Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

no code implementations13 Nov 2019 Wankai Tang, Ming Zheng Chen, Xiangyu Chen, Jun Yan Dai, Yu Han, Marco Di Renzo, Yong Zeng, Shi Jin, Qiang Cheng, Tie Jun Cui

The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.

Nonnegative Matrix Factorization with Local Similarity Learning

no code implementations9 Jul 2019 Chong Peng, Zhao Kang, Chenglizhao Chen, Qiang Cheng

Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data.


Automated Classification of Seizures against Nonseizures: A Deep Learning Approach

no code implementations5 Jun 2019 Xinghua Yao, Qiang Cheng, Guo-Qiang Zhang

In order to capture essential seizure features, this paper integrates an emerging deep learning model, the independently recurrent neural network (IndRNN), with a dense structure and an attention mechanism to exploit temporal and spatial discriminating features and overcome seizure variabilities.

Classification EEG +2

Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data

no code implementations16 Apr 2019 Chong Peng, Qiang Cheng

As a special case we focus on a quadratic model that admits a closed-form analytical solution.


A Novel Independent RNN Approach to Classification of Seizures against Non-seizures

no code implementations22 Mar 2019 Xinghua Yao, Qiang Cheng, Guo-Qiang Zhang

In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions.

Classification EEG +1

Lie Group Auto-Encoder

no code implementations28 Jan 2019 Liyu Gong, Qiang Cheng

Moreover, we derive an intrinsic loss for UTDAT Lie group which can be calculated as l-2 loss in the tangent space.


Exploiting Edge Features in Graph Neural Networks

no code implementations7 Sep 2018 Liyu Gong, Qiang Cheng

The proposed framework can consolidate current graph neural network models; e. g. graph convolutional networks (GCN) and graph attention networks (GAT).

General Classification Graph Attention +3

Unified Spectral Clustering with Optimal Graph

1 code implementation12 Nov 2017 Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu

Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers.

Clustering graph construction

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

no code implementations1 May 2017 Zhao Kang, Chong Peng, Qiang Cheng

Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering.


Top-N Recommendation on Graphs

1 code implementation27 Sep 2016 Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng

To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix.

Collaborative Filtering Recommendation Systems

Top-N Recommendation with Novel Rank Approximation

1 code implementation25 Feb 2016 Zhao Kang, Qiang Cheng

Some empirical results demonstrate that it can provide a better approximation to original problems than convex relaxation.

Recommendation Systems

Top-N Recommender System via Matrix Completion

no code implementations19 Jan 2016 Zhao Kang, Chong Peng, Qiang Cheng

Top-N recommender systems have been investigated widely both in industry and academia.

Matrix Completion Recommendation Systems

Robust PCA via Nonconvex Rank Approximation

1 code implementation17 Nov 2015 Zhao Kang, Chong Peng, Qiang Cheng

This approximation to the matrix rank is tighter than the nuclear norm.

Robust Subspace Clustering via Tighter Rank Approximation

1 code implementation30 Oct 2015 Zhao Kang, Chong Peng, Qiang Cheng

For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.

Clustering Face Clustering +1

Robust Subspace Clustering via Smoothed Rank Approximation

1 code implementation18 Aug 2015 Zhao Kang, Chong Peng, Qiang Cheng

However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.

Clustering Face Clustering +1

Variational Planning for Graph-based MDPs

no code implementations NeurIPS 2013 Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler

The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph.

Decision Making

Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework

no code implementations NeurIPS 2010 Hongbo Zhou, Qiang Cheng

In this paper, we propose a robust minimax framework to interpret the relationship between data and regularization terms for a large class of loss functions.

Generalization Bounds

Cannot find the paper you are looking for? You can Submit a new open access paper.