NDLI: Discrimination projective dictionary pair methods in dictionary learning1

Content Provider	IEEE Xplore Digital Library
Author	Chen, Xiuhong Gao, Jiaxue
Copyright Year	2015
Description	Author affiliation: School of Digital Media, Jiangnan University, Wuxi 214122, Jiangsu, China (Chen, Xiuhong; Gao, Jiaxue)
Abstract	Recently, the projective dictionary pair learning (DPL) method has obtained better classification representation accuracy which learned a synthesis dictionary and an analysis dictionary to achieve the goal of signal representation. In order to obtain more discriminative ability of the dictionary pair, a new method based on DPL, called discrimination projective dictionary pair learning based dictionary learning method (DPDPL), will be proposed. In DPDPL, we will consider both the inter-class and intra-class incoherence constraints of the synthesis dictionary, and the analysis dictionary should be used to simultaneously maximize the total scatter and the between-class scatter of the signal after coding. Thus, the method ensures that the dictionary has better discriminative ability and the signals are more separable after coding. Experiments of sparse representation-based classification on several face databases show the good performances of the proposed dictionary learning method.
Starting Page	204
Ending Page	208
File Size	316277
Page Count	5
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7407876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Analytical models Dictionaries Image coding analysis dictionary pair learning (DPL) image classification Coherence Encoding dictionary learning (DL) Image reconstruction sparse representation incoherent constraint
Content Type	Text
Resource Type	Article

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