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Given a corrupted low-rank matrix, robust principal component analysis performs a low-rank-plus-sparse matrix decomposition by solving a convex program. In this paper we first develop an efficient rank-revealing decomposition algorithm aided by randomization, which provides information about the singular subspaces and singular values of a given data matrix. The proposed factorization termed randomized...
In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by the randomized sampling scheme and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Simulation results...
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