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Creators/Authors contains: "Man-Cho So, Anthony"

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  1. ; ; ; (, Internationl Conference on Machine Learning)
    The K-subspaces (KSS) method is a generalization of the K-means method for subspace clustering. In this work, we present local convergence analysis and a recovery guarantee for KSS, assuming data are generated by the semi-random union of subspaces model, where N points are randomly sampled from K ≥ 2 overlapping subspaces. We show that if the initial assignment of the KSS method lies within a neighborhood of a true clustering, it converges at a superlinear rate and finds the correct clustering within (log logN) iterations with high probability. Moreover, we propose a thresholding inner-product based spectral method for initialization and prove that it produces a point in this neighborhood. We also present numerical results of the studied method to support our theoretical developments. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, SIAM Journal on Optimization)
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