Robust Subspace Clustering

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Robust Subspace Clustering (2013)

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by Mahdi Soltanolkotabi , Ehsan Elhamifar , Emmanuel J. Candes

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BibTeX

@MISC{Soltanolkotabi13robustsubspace,
    author = {Mahdi Soltanolkotabi and Ehsan Elhamifar and Emmanuel J. Candes},
    title = {Robust Subspace Clustering},
    year = {2013}
}

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Abstract

Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [17] to cluster noisy data, and develops some novel theory demonstrating its correctness. In particular, the theory uses ideas from geometric functional analysis to show that the algorithm can accurately recover the underlying subspaces under minimal requirements on their orientation, and on the number of samples per subspace. Synthetic as well as real data experiments complement our theoretical study, illustrating our approach and demonstrating its effectiveness.

Keyphrases

robust subspace clustering theoretical study high-dimensional space multi-subspace representation novel theory sparse subspace clustering geometric functional analysis minimal requirement real data experiment noisy data underlying subspace

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