Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Sprechmann, P and Bronstein, AM and Sapiro, G},
title = {Learning Robust Low-Rank Representations},
url = {},

RIS format (EndNote, RefMan)

AB - In this paper we present a comprehensive framework for learning robustlow-rank representations by combining and extending recent ideas for learningfast sparse coding regressors with structured non-convex optimizationtechniques. This approach connects robust principal component analysis (RPCA)with dictionary learning techniques and allows its approximation via trainableencoders. We propose an efficient feed-forward architecture derived from anoptimization algorithm designed to exactly solve robust low dimensionalprojections. This architecture, in combination with different trainingobjective functions, allows the regressors to be used as online approximants ofthe exact offline RPCA problem or as RPCA-based neural networks. Simplemodifications of these encoders can handle challenging extensions, such as theinclusion of geometric data transformations. We present several examples withreal data from image, audio, and video processing. When used to approximateRPCA, our basic implementation shows several orders of magnitude speedupcompared to the exact solvers with almost no performance degradation. We showthe strength of the inclusion of learning to the RPCA approach on a musicsource separation application, where the encoders outperform the exact RPCAalgorithms, which are already reported to produce state-of-the-art results on abenchmark database. Our preliminary implementation on an iPad showsfaster-than-real-time performance with minimal latency.
AU - Sprechmann,P
AU - Bronstein,AM
AU - Sapiro,G
TI - Learning Robust Low-Rank Representations
UR -
ER -