Improving K-Subspaces via Coherence Pursuit

Subspace clustering is a powerful generalization of clustering for high-dimensional data analysis, where low-rank cluster structure is leveraged for accurate inference. K-Subspaces (KSS), an alternating algorithm that mirrors K-means, is a classical approach for clustering with this model. Like K-me...

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Veröffentlicht in:	IEEE journal of selected topics in signal processing 2018-12, Vol.12 (6), p.1575-1588
Hauptverfasser:	Gitlin, Andrew, Tao, Biaoshuai, Balzano, Laura, Lipor, John
Format:	Artikel
Sprache:	eng
Schlagworte:	<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> K</tex-math> </inline-formula>-Subspaces (KSS) Algorithm design and analysis Clustering algorithms coherence pursuit (CoP) Principal component analysis robust principal component analysis (PCA) robust subspace recovery (RSR) Robustness Signal processing algorithms Subspace clustering
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