Abstract
We propose a proximal point algorithm to solve the LAROS problem, that is, the problem of finding a “large approximately rank-one submatrix.” This LAROS problem is used to sequentially extract features in data. We also develop new stopping criteria for the proximal point algorithm, which is based on the duality conditions of $\epsilon$-optimal solutions of the LAROS problem, with a theoretical guarantee. We test our algorithm with two image databases and show that we can use the LAROS problem to extract appropriate common features from these images.
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