  We propose a proximal point algorithm to solve 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 a new
stopping criterion for the proximal point algorithm, which is based on the
duality conditions of \eps-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.
