Scaled Simplex Representation for Subspace Clustering

The self-expressive property of data points, that is, each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering (SC) methods. Most self-expressive methods usually construct a feasible affinity matrix from a coeffici...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on cybernetics Jg. 51; H. 3; S. 1493 - 1505
Hauptverfasser:	Xu, Jun, Yu, Mengyang, Shao, Ling, Zuo, Wangmeng, Meng, Deyu, Zhang, Lei, Zhang, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Affinity Algorithms Clustering Clustering algorithms Coefficients Computational modeling Constraints Correlation Data models Data points Iterative methods Mathematical analysis Matrix methods Optimization Representations Scaled simplex representation (SSR) self-expressiveness Statistical analysis subspace clustering (SC) Subspaces
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!