Analysis-Synthesis dictionary learning for universality-particularity representation based classification

Meng Yang, Weiyang Liu, Weixin Luo, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

31 Citations (Scopus)

Abstract

Dictionary learning has played an important role in the success of sparse representation. Although synthesis dictionary learning for sparse representation has been well studied for universality representation (i.e., the dictionary is universal to all classes) and particularity representation (i.e., the dictionary is class-particular), jointly learning an analysis dictionary and a synthesis dictionary is still in its infant stage. Universality-particularity representation can well match the intrinsic characteristics of data (i.e., different classes share commonality and distinctness), while analysis-synthesis dictionary can give a more complete view of data representation (i.e., analysis dictionary is a dual-viewpoint of synthesis dictionary). In this paper, we proposed a novel model of analysis-synthesis dictionary learning for universalityparticularity (ASDL-UP) representation based classification. The discrimination of universality and particularity representation is jointly exploited by simultaneously learning a pair of analysis dictionary and synthesis dictionary. More specifically, we impose a label preserving term to analysis coding coefficients for universality representation. Fisher-like regularizations for analysis coding coefficients and the subsequent synthesis representation are introduced to particularity representation. Compared with other state-of-The-Art dictionary learning methods, ASDL-UP has shown better or competitive performance in various classification tasks.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI Press
Pages2251-2257
Number of pages7
ISBN (Electronic)9781577357605
Publication statusPublished - 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period12/02/1617/02/16

ASJC Scopus subject areas

  • Artificial Intelligence

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