锘??xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:trackback="http://madskills.com/public/xml/rss/module/trackback/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/"><channel><title>&#x4E9A;&#x6D32;&#x76;&#x61;&#x56FD;&#x4EA7;&#x76;&#x61;&#x5929;&#x5802;&#x76;&#x61;&#x4E45;&#x4E45;&#x2C;&#x4E45;&#x4E45;&#x4E45;&#x4E45;&#x4E45;&#x4E45;&#x7EFC;&#x5408;&#x65E5;&#x672C;&#x2C;&#x36;&#x39;&#x56FD;&#x4EA7;&#x6210;&#x4EBA;&#x7EFC;&#x5408;&#x4E45;&#x4E45;&#x7CBE;&#x54C1;</title><link>http://m.shnenglu.com/aa19870406/archive/2011/03/20/142253.html</link><dc:creator>MrRightLeft</dc:creator><author>MrRightLeft</author><pubDate>Sun, 20 Mar 2011 01:23:00 GMT</pubDate><guid>http://m.shnenglu.com/aa19870406/archive/2011/03/20/142253.html</guid><wfw:comment>http://m.shnenglu.com/aa19870406/comments/142253.html</wfw:comment><comments>http://m.shnenglu.com/aa19870406/archive/2011/03/20/142253.html#Feedback</comments><slash:comments>0</slash:comments><wfw:commentRss>http://m.shnenglu.com/aa19870406/comments/commentRss/142253.html</wfw:commentRss><trackback:ping>http://m.shnenglu.com/aa19870406/services/trackbacks/142253.html</trackback:ping><description><![CDATA[1.鏈�嫻佽鐨勬柟娉曪細涓繪垚鍒嗗垎鏋?Principal Component Analisis,PCA)鍜岀嚎鎬у垽鍒垎鏋?Linear Discriminant Analysis,LDA)<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;鐩稿叧璁烘枃錛歅CA ,Face Recognition using Eigenfaces&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LDA,Based on an optimized LDA algorithm for face recognition<br><br>2.嫻佸艦瀛︿範綆楁硶:&nbsp; 絳夎窛紱繪槧灝?Isometric mapping,Isomap),灞�閮ㄧ嚎鎬у祵鍏?locally linear embedding, LLE),鎷夋櫘鎷夋柉鐗瑰緛鏄犲皠(laplacian eigenmap)鍜屽眬閮ㄤ繚鎸佹姇褰?Locality Preserving Projections,LPP)絳?br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 鐩稿叧璁烘枃:&nbsp; Isomap,&nbsp; global geometric framework for nonlinear dimensionality reduction&nbsp;&nbsp;&nbsp; LLE, Nonlinear dimentionality reduction by locally linear embedding.&nbsp;&nbsp;&nbsp; laplacian eigenmap,&nbsp;Laplacian eigenmaps for dimensionality reduction and data representation&nbsp;.&nbsp;&nbsp;&nbsp; LPP, Learning a locality discriminanting projection for classification.&nbsp;&nbsp; <br><br><!--startfragment -->
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