DocumentCode :
148980
Title :
On the need for metrics in dictionary learning assessment
Author :
Chevallier, Sylvain ; Barthelemy, Quentin ; Atif, Jamal
Author_Institution :
LISV, Univ. of Versailles, Versailles, France
fYear :
2014
fDate :
1-5 Sept. 2014
Firstpage :
1427
Lastpage :
1431
Abstract :
Dictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields. Albeit their success, the criteria introduced so far for the assessment of their performances suffer from several shortcomings. The scope of this paper is to conduct a thorough analysis of these criteria and to highlight the need for principled criteria, enjoying the properties of metrics. Henceforth we introduce new criteria based on transportation like metrics and discuss their behaviors w.r.t the literature.
Keywords :
learning (artificial intelligence); signal processing; dictionary learning assessment; signal processing community; Atomic measurements; Convergence; Dictionaries; Signal to noise ratio; Training; Transportation; Dictionary learning; detection rate; dictionary recovering; metric; transportation distance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
Type :
conf
Filename :
6952505
Link To Document :
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