NDLI: Linear-Time Subspace Clustering via Bipartite Graph Modeling

Content Provider	IEEE Xplore Digital Library
Author	Adler, A. Elad, M. Hel-Or, Y.
Copyright Year	2012
Abstract	We present a linear-time subspace clustering approach that combines sparse representations and bipartite graph modeling. The signals are modeled as drawn from a union of low-dimensional subspaces, and each signal is represented by a sparse combination of basis elements, termed atoms, which form the columns of a dictionary matrix. The sparse representation coefficients are arranged in a sparse affinity matrix, which defines a bipartite graph of two disjoint sets: (1) atoms and (2) signals. Subspace clustering is obtained by applying low-complexity spectral bipartite graph clustering that exploits the small number of atoms for complexity reduction. The complexity of the proposed approach is linear in the number of signals, thus it can rapidly cluster very large data collections. Performance evaluation of face clustering and temporal video segmentation demonstrates comparable clustering accuracies to state-of-the-art at a significantly lower computational load.
Page Count	13
File Size	3808185
Starting Page	2234
Ending Page	2246
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	10
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	Dictionaries Complexity theory Sparse matrices Bipartite graph Clustering algorithms Optimization Matrix decomposition temporal video segmentation. dictionary face clustering sparse representation subspace clustering temporal video segmentation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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