Robust Principal Component Analysis using Facial Reduction

We study algorithms for robust principal component analysis (RPCA) for a partially observed data matrix. The aim is to recover the data matrix as a sum of a low-rank matrix and a sparse matrix so as to eliminate erratic noise (outliers). This problem is known to be NP-hard in general. A classical way to solve … Read more

A Branch-and-Price Approach to the k-Clustering Minimum Biclique Completion Problem

Given a bipartite graph G = (S , T , E ), we consider the problem of finding k bipartite subgraphs, called clusters, such that each vertex i of S appears in exactly one of them, every vertex j of T appears in each cluster in which at least one of its neighbors appears, and … Read more

Nuclear norm minimization for the planted clique and biclique problems

We consider the problems of finding a maximum clique in a graph and finding a maximum-edge biclique in a bipartite graph. Both problems are NP-hard. We write both problems as matrix-rank minimization and then relax them using the nuclear norm. This technique, which may be regarded as a generalization of compressive sensing, has recently been … Read more