Latent Space Sparse and Low-Rank Subspace Clustering

We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels a...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2015-06, Vol.9 (4), p.691-701
Main Authors: Patel, Vishal M., Van Nguyen, Hien, Vidal, Rene
Format: Article
Language:English
Subjects:
Clustering algorithms
Clustering methods
Cost function
Dimension reduction
Kernel
kernel methods
low-rank subspace clustering
manifold clustering
Signal processing algorithms
Sparse matrices
sparse subspace clustering
subspace clustering
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Summary:We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representations. Efficient optimization methods are proposed and their non-linear extensions based on kernel methods are presented. Various experiments show that the proposed methods perform better than many competitive subspace clustering methods.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2015.2402643