Subspace Clustering via Good Neighbors

Subspace Clustering via Good Neighbors

Finding the informative subspaces of high-dimensional datasets is at the core of numerous applications in computer vision, where spectral-based subspace clustering is arguably the most widely studied method due to its strong empirical performance. Such algorithms first compute an affinity matrix to...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 42(2020), 6 vom: 14. Juni, Seite 1537-1544
1. Verfasser: Yang, Jufeng (VerfasserIn)
Weitere Verfasser: Liang, Jie, Wang, Kai, Rosin, Paul L, Yang, Ming-Hsuan
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2020
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article
LEADER 01000naa a22002652 4500
001 NLM296759910
003 DE-627
005 20231225090533.0
007 cr uuu---uuuuu
008 231225s2020 xx |||||o 00| ||eng c
024 7 |a 10.1109/TPAMI.2019.2913863  |2 doi 
028 5 2 |a pubmed24n0989.xml 
035 |a (DE-627)NLM296759910 
035 |a (NLM)31056488 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Yang, Jufeng  |e verfasserin  |4 aut 
245 1 0 |a Subspace Clustering via Good Neighbors 
264 1 |c 2020 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 11.05.2020 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Finding the informative subspaces of high-dimensional datasets is at the core of numerous applications in computer vision, where spectral-based subspace clustering is arguably the most widely studied method due to its strong empirical performance. Such algorithms first compute an affinity matrix to construct a self-representation for each sample using other samples as a dictionary. Sparsity and connectivity of the self-representation play important roles in effective subspace clustering. However, simultaneous optimization of both factors is difficult due to their conflicting nature, and most existing methods are designed to address only one factor. In this paper, we propose a post-processing technique to optimize both sparsity and connectivity by finding good neighbors. Good neighbors induce key connections among samples within a subspace and not only have large affinity coefficients but are also strongly connected to each other. We reassign the coefficients of the good neighbors and eliminate other entries to generate a new coefficient matrix. We show that the few good neighbors can effectively recover the subspace, and the proposed post-processing step of finding good neighbors is complementary to most existing subspace clustering algorithms. Experiments on five benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods with negligible additional computation cost 
650 4 |a Journal Article 
700 1 |a Liang, Jie  |e verfasserin  |4 aut 
700 1 |a Wang, Kai  |e verfasserin  |4 aut 
700 1 |a Rosin, Paul L  |e verfasserin  |4 aut 
700 1 |a Yang, Ming-Hsuan  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on pattern analysis and machine intelligence  |d 1979  |g 42(2020), 6 vom: 14. Juni, Seite 1537-1544  |w (DE-627)NLM098212257  |x 1939-3539  |7 nnns 
773 1 8 |g volume:42  |g year:2020  |g number:6  |g day:14  |g month:06  |g pages:1537-1544 
856 4 0 |u http://dx.doi.org/10.1109/TPAMI.2019.2913863  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 42  |j 2020  |e 6  |b 14  |c 06  |h 1537-1544