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Sparse subspace clustering
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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. Our key contribution is to show that, under mild assumptions, the SR can be obtained 'exactly' by using 1 optimization. The segmentation of
doi:10.1109/cvprw.2009.5206547
fatcat:3yogs2jzirhl3npbdj4i2f7rmq