Use of PALM for ℓ1 sparse matrix factorization: Difficulty and rationalization of a two-step approach
Abstract
Blind Source Separation (BSS) is a key machine learning method, which has been successfully applied to analyze multichannel data in various domains ranging from medical imaging to astrophysics. Being an ill-posed matrix factorization problem, it is necessary to introduce extra regularizing priors on the sources. While using sparsity has led to improved factorization results, the quality of the separation process turns out to be dramatically...
Paper Details
Title
Use of PALM for ℓ1 sparse matrix factorization: Difficulty and rationalization of a two-step approach
Published Date
Feb 1, 2020
Journal
Volume
97
Pages
102611 - 102611
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