Noise aware analysis operator learning for approximately cosparse signals

Mehrdad Yaghoobi, M.E. Davies, S. Nam, R. Gribonval

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper investigates analysis operator learning for the recently introduced cosparse signal model that is a natural analysis complement to the more traditional sparse signal model. Previous work on such analysis operator learning has relied on access to a set of clean training samples. Here we introduce a new learning framework which can use training data which is corrupted by noise and/or is only approximately cosparse. The new model assumes that a p-cosparse signal exists in an epsilon neighborhood of each data point. The operator is assumed to be uniformly normalized tight frame (UNTF) to exclude some trivial operators. In this setting, an alternating optimization algorithm is introduced to learn a suitable analysis operator.
Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages5409-5412
Number of pages4
DOIs
Publication statusPublished - 1 Jan 2012

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