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20152022
most citedLogDet Rank Minimization with Application to Subspace Clustering

41 citations · 60 across the 11 of their papers we have counts for

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10 papers · 1 filter

cs.LG20221 cited

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

Xinxing Wu, Chong Peng, Richard Charnigo +1

Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy,…

cs.LG2022

Log-based Sparse Nonnegative Matrix Factorization for Data Representation

Chong Peng, Yiqun Zhang, Yongyong Chen +3

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

cs.LG2020

Structured Graph Learning for Clustering and Semi-supervised Classification

Zhao Kang, Chong Peng, Qiang Cheng +4

Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised cla…

cs.LG20203 cited

Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering

Chong Peng, Zhilu Zhang, Zhao Kang +2

In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously…

cs.LG20191 cited

Structure Learning with Similarity Preserving

Zhao Kang, Xiao Lu, Yiwei Lu +2

Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications. However, in man…

cs.LG20196 cited

Nonnegative Matrix Factorization with Local Similarity Learning

Chong Peng, Zhao Kang, Chenglizhao Chen +1

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…