{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T14:14:47Z","timestamp":1648649687900},"reference-count":15,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:p> Dictionary learning (DL) algorithms have shown very good performance in face recognition. However, conventional DL algorithms exploit only the training samples to obtain the dictionary and totally neglect the test sample in the learning procedure. As a result, if DL is associated with the linear representation of test sample, DL may be able to perform better in classifying the test samples than conventional DL algorithms. In this paper, we propose a test sample oriented dictionary learning (TSODL) algorithm for face recognition. We combine the linear representation (including the [Formula: see text]-norm, [Formula: see text]-norm and [Formula: see text]-norm) of a test sample and the basic model of DL to learn a single dictionary for each test sample. Thus, it can simultaneously obtain the dictionary and representation coefficients of the test sample by minimizing only one objective function. In order to make the learning procedure more efficient, we initialize a dictionary for the new test sample by selecting from the dictionaries of previous test samples. The experimental results show that the TSODL algorithm can classify test samples more accurately than some of the state-of-the-art DL and sparse coding algorithms by using a linear classifier method on three public face databases. <\/jats:p>","DOI":"10.1142\/s0218126616500171","type":"journal-article","created":{"date-parts":[[2015,10,15]],"date-time":"2015-10-15T08:25:24Z","timestamp":1444897524000},"page":"1650017","source":"Crossref","is-referenced-by-count":3,"title":["Test Sample Oriented Dictionary Learning for Face Recognition"],"prefix":"10.1142","volume":"25","author":[{"given":"Zhengming","family":"Li","sequence":"first","affiliation":[{"name":"Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China"},{"name":"Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2016,2,2]]},"reference":[{"key":"S0218126616500171BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.128"},{"key":"S0218126616500171BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"S0218126616500171BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.860430"},{"key":"S0218126616500171BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.911828"},{"key":"S0218126616500171BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2010.939537"},{"key":"S0218126616500171BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.11.014"},{"key":"S0218126616500171BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.88"},{"key":"S0218126616500171BIB019","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2090535"},{"key":"S0218126616500171BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"issue":"3","key":"S0218126616500171BIB022","first-page":"1108","volume":"22","author":"Liu J.","year":"2013","journal-title":"IEEE Trans. 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