LEADER 03446nam 22006135 450 001 9910298969003321 005 20200704081240.0 010 $a3-319-07416-4 024 7 $a10.1007/978-3-319-07416-0 035 $a(CKB)3710000000227346 035 $a(EBL)1802522 035 $a(SSID)ssj0001338454 035 $a(PQKBManifestationID)11780363 035 $a(PQKBTitleCode)TC0001338454 035 $a(PQKBWorkID)11337922 035 $a(PQKB)11507716 035 $a(MiAaPQ)EBC1802522 035 $a(DE-He213)978-3-319-07416-0 035 $a(PPN)180624008 035 $a(EXLCZ)993710000000227346 100 $a20140828d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust Recognition via Information Theoretic Learning /$fby Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (120 p.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $aDescription based upon print version of record. 311 $a1-322-13665-3 311 $a3-319-07415-6 320 $aIncludes bibliographical references. 327 $aIntroduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ?1 Regularized Correntropy -- Correntropy with Nonnegative Constraint. 330 $aThis Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aOptical data processing 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 615 0$aOptical data processing. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aImage Processing and Computer Vision. 676 $a006.3 676 $a006.37 700 $aHe$b Ran$4aut$4http://id.loc.gov/vocabulary/relators/aut$0929219 702 $aHu$b Baogang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYuan$b Xiaotong$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWang$b Liang$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298969003321 996 $aRobust Recognition via Information Theoretic Learning$92088436 997 $aUNINA