DocumentCode :
2795103
Title :
Clustering disjoint subspaces via sparse representation
Author :
Elhamifar, Ehsan ; Vidal, René
Author_Institution :
Center for Imaging Sci., Johns Hopkins Univ., Baltimore, MD, USA
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
1926
Lastpage :
1929
Abstract :
Given a set of data points drawn from multiple low-dimensional linear subspaces of a high-dimensional space, we consider the problem of clustering these points according to the subspaces they belong to. Our approach exploits the fact that each data point can be written as a sparse linear combination of all the other points. When the subspaces are independent, the sparse coefficients can be found by solving a linear program. However, when the subspaces are disjoint, but not independent, the problem becomes more challenging. In this paper, we derive theoretical bounds relating the principal angles between the subspaces and the distribution of the data points across all the subspaces under which the coefficients are guaranteed to be sparse. The clustering of the data is then easily obtained from the sparse coefficients. We illustrate the validity of our results through simulation experiments.
Keywords :
pattern clustering; data clustering; disjoint subspace clustering; sparse coefficients; sparse linear combination; sparse representation; Application software; Clustering methods; Computer vision; Image processing; Image segmentation; Signal processing; Sparse matrices; Statistical analysis; Video sequences; Subspace clustering; disjoint subspaces; sparsity; subspace angles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495317
Filename :
5495317
Link To Document :
بازگشت