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Learning overcomplete dictionaries with ℓ0-sparse Non-negative Matrix Factorisation

O'Hanlon, K and Plumbley, MD (2013) Learning overcomplete dictionaries with ℓ0-sparse Non-negative Matrix Factorisation In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2013-12-03 - 2013-12-05, Austin, TX.

OHanlonPlumbley13-globalsip_accepted_notice.pdf - Accepted version Manuscript

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Non-negative Matrix Factorisation (NMF) is a popular tool in which a ‘parts-based’ representation of a non-negative matrix is sought. NMF tends to produce sparse decompositions. This sparsity is a desirable property in many applications, and Sparse NMF (S-NMF) methods have been proposed to enhance this feature. Typically these enforce sparsity through use of a penalty term, and a `1 norm penalty term is often used. However an `1 penalty term may not be appropriate in a non-negative framework. In this paper the use of a `0 norm penalty for NMF is proposed, approximated using backwards elimination from an initial NNLS decomposition. Dictionary recovery experiments using overcomplete dictionaries show that this method outperforms both NMF and a state of the art S-NMF method, in particular when the dictionary to be learnt is dense.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
O'Hanlon, K
Date : 1 January 2013
DOI : 10.1109/GlobalSIP.2013.6737056
Copyright Disclaimer : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Contributors :
Uncontrolled Keywords : sparse, non-negative, dictionary learning, NMF
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:21
Last Modified : 16 Jan 2019 18:43

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