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Finding the largest low-rank clusters with Ky Fan 2-k-norm and l1-norm

Xuan Vinh Doan(Xuan.Doan***at***wbs.ac.uk)
Stephen Vavasis(vavasis***at***math.uwaterloo.ca)

Abstract: We propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to fi nd k largest approximately rank-one submatrix blocks of a given nonnegative matrix that has low-rank block diagonal structure with noise. We analyze low-rank and sparsity structures of the optimal solutions using properties of these two matrix norms. We show that, under certain hypotheses, with high probability, the approach can recover rank-one submatrix blocks even when they are corrupted with random noise and inserted into a much larger matrix with other random noise blocks.

Keywords: low-rank clustering, Ky Fan 2-k-norm, nonnegative matrix factorization

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation:

Download: [PDF]

Entry Submitted: 03/24/2014
Entry Accepted: 03/24/2014
Entry Last Modified: 03/24/2014

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