Projection-preserving block-diagonal low-rank representation for subspace clustering

Zisen Kong; Dongxia Chang; Zhiqiang Fu; Jiapeng Wang; Yiming Wang; Yao Zhao

doi:10.1016/j.neucom.2023.01.051

ScienceGate Book Chapters

JOURNAL ARTICLE

Projection-preserving block-diagonal low-rank representation for subspace clustering

Zisen Kong Dongxia Chang Zhiqiang Fu Jiapeng Wang Yiming Wang Yao Zhao

Year: 2023 Journal: Neurocomputing Vol: 526 Pages: 19-29 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2023.01.051

Get Full-Text PDF Get Analytical Report

Keywords:

Cluster analysis Block matrix Pattern recognition (psychology) Projection (relational algebra) Computer science Representation (politics) Subspace topology Artificial intelligence Diagonal Noise (video) Mathematics Algorithm Linear subspace

Metrics

Cited By

5.71

FWCI (Field Weighted Citation Impact)

Refs

0.94

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Projection-preserving block-diagonal low-rank representation for subspace clustering

Metrics

Citation History

Topics

Related Documents

Adaptive low-rank kernel block diagonal representation subspace clustering

Rank-Constrained Block Diagonal Representation for Subspace Clustering

Multi-geometric block diagonal representation subspace clustering with low-rank kernel

Subspace Clustering by Block Diagonal Representation

Membership representation for detecting block-diagonal structure in low-rank or sparse subspace clustering