﻿<?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>C++博客-aa19870406-随笔分类-图像处理与模式识别</title><link>http://www.cppblog.com/aa19870406/category/16325.html</link><description /><language>zh-cn</language><lastBuildDate>Sun, 20 Mar 2011 01:45:58 GMT</lastBuildDate><pubDate>Sun, 20 Mar 2011 01:45:58 GMT</pubDate><ttl>60</ttl><item><title>人脸识别学习路线</title><link>http://www.cppblog.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://www.cppblog.com/aa19870406/archive/2011/03/20/142253.html</guid><wfw:comment>http://www.cppblog.com/aa19870406/comments/142253.html</wfw:comment><comments>http://www.cppblog.com/aa19870406/archive/2011/03/20/142253.html#Feedback</comments><slash:comments>0</slash:comments><wfw:commentRss>http://www.cppblog.com/aa19870406/comments/commentRss/142253.html</wfw:commentRss><trackback:ping>http://www.cppblog.com/aa19870406/services/trackbacks/142253.html</trackback:ping><description><![CDATA[1.最流行的方法：主成分分析(Principal Component Analisis,PCA)和线性判别分析(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;相关论文：PCA ,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.流形学习算法:&nbsp; 等距离映射(Isometric mapping,Isomap),局部线性嵌入(locally linear embedding, LLE),拉普拉斯特征映射(laplacian eigenmap)和局部保持投影(Locality Preserving Projections,LPP)等<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; 相关论文:&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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<img src ="http://www.cppblog.com/aa19870406/aggbug/142253.html" width = "1" height = "1" /><br><br><div align=right><a style="text-decoration:none;" href="http://www.cppblog.com/aa19870406/" target="_blank">MrRightLeft</a> 2011-03-20 09:23 <a href="http://www.cppblog.com/aa19870406/archive/2011/03/20/142253.html#Feedback" target="_blank" style="text-decoration:none;">发表评论</a></div>]]></description></item></channel></rss>