标题:Fisher Discriminative Coupled Dictionaries Learning
作者:Shan, Tingting; Jiang, Mingyan
作者机构:[Shan, Tingting; Jiang, Mingyan] Shandong Univ, Sch Informat Sci & Engn, Jimo Binhai Rd 72, Qingdao 266237, Shandong, Peoples R China.
通讯作者:Jiang, Mingyan;Jiang, MY
通讯作者地址:[Jiang, MY]Shandong Univ, Sch Informat Sci & Engn, Jimo Binhai Rd 72, Qingdao 266237, Shandong, Peoples R China.
来源:NEURAL PROCESSING LETTERS
出版年:2019
卷:50
期:3
页码:2991-3008
DOI:10.1007/s11063-019-10015-x
关键词:Dictionary learning; Collaborative representation; Sparse; representation; Fisher discrimination; Face recognition
摘要:As a recently proposed technique, dictionary learning (DL) has been extensively studied in the field of pattern recognition. Most scholars use a sparse representation as the basic formula for DL while incorporating other techniques into the DL process for obtain an expected dictionary, and exploring a problem with an l0-norm or l1-norm. However, these strategies increase the time complexity and require additional classifier-aided classification work. In this paper, we propose a novel form of DL called Fisher discriminative coupled dictionaries learning based on general dictionary learning. We use an l2-norm to improve the training speed. On embedding the Fisher discrimination into the process of DL, the updated dictionary contains the discriminant information. We update the sample dictionary and coefficient projection dictionary simultaneously as a "dictionary pair". The sample dictionary is used directly for image classification. The superiority of the proposed method is proven through exhaustive experiments on the AR, extended Yale-B, Scene 15, and Caltech-101 databases.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062939474&doi=10.1007%2fs11063-019-10015-x&partnerID=40&md5=6c390e8026aab78fe0097be260c2b59e
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