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A nonconvex approach to low-rank and sparse matrix decomposition with application to video surveillance. (arXiv:1807.01276v1 [math.OC])
来源于:arXiv
In this paper, we develop a new nonconvex approach to the problem of low-rank
and sparse matrix decomposition. In our nonconvex method, we replace the rank
function and the $\ell_{0}$-norm of a given matrix with a non-convex fraction
function on the singular values and the elements of the matrix respectively. An
alternative direction method of multipliers algorithm is utilized to solve our
nonconvex problem with the non-convex fraction function penalty. Numerical
experiments on video surveillance show that our method performs very well in
separating the moving objects from the static background. 查看全文>>