Sparse subspace clustering


We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points. In general, finding such a SR is NP hard… (More)
DOI: 10.1109/CVPRW.2009.5206547


4 Figures and Tables


Citations per Year

994 Citations

Semantic Scholar estimates that this publication has 994 citations based on the available data.

See our FAQ for additional information.

Slides referencing similar topics