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Content Provider | IEEE Xplore Digital Library |
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Author | Patel, V.M. Hien Van Nguyen Vidal, R. |
Copyright Year | 2007 |
Abstract | 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. |
Sponsorship | IEEE Signal Processing Society |
Starting Page | 691 |
Ending Page | 701 |
Page Count | 11 |
File Size | 3130077 |
File Format | |
ISSN | 19324553 |
Volume Number | 9 |
Issue Number | 4 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-01-01 |
Publisher Place | U.S.A. |
Access Restriction | One Nation One Subscription (ONOS) |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Clustering algorithms Sparse matrices Kernel Signal processing algorithms Clustering methods Cost function manifold clustering Dimension reduction kernel methods subspace clustering low-rank subspace clustering sparse subspace clustering |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering |
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