資源簡介
基于局部Fisher準則的非線性核Fisher辨別分析,應用于有監督的特征提取與高維數據的有效降維。
代碼片段和文件信息
%?test_KLFDA.m
%
%?(c)?Masashi?Sugiyama?Department?of?Compter?Science?Tokyo?Institute?of?Technology?Japan.
%?????sugi@cs.titech.ac.jp?????http://sugiyama-www.cs.titech.ac.jp/~sugi/software/LFDA/
clear?all;
rand(‘state‘0);
randn(‘state‘0);
%%%%%%%%%%%%%%%%%%%%%%?Generating?data
n1a=100;
n1b=100;
n2=100;
X1a=[randn(2n1a).*repmat([1;2][1?n1a])+repmat([-6;0][1?n1a])];
X1b=[randn(2n1b).*repmat([1;2][1?n1b])+repmat([?6;0][1?n1b])];
X2=?[randn(2n2?).*repmat([1;2][1?n2?])+repmat([?0;0][1?n2?])];
X=[X1a?X1b?X2];
Y=[ones(n1a+n1b1);2*ones(n21)];
%%%%%%%%%%%%%%%%%%%%%%?Computing?LFDA?solution
%Gaussian?kernel
K1=Kmatrix_Gauss(X1);
[T1Z1]=KLFDA(K1Y1);
%?linear?kernel
K2=X‘*X;
[T2Z2]=KLFDA(K2Y1);
%%%%%%%%%%%%%%%%%%%%%%?Displaying?original?2D?d
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件???????2471??2009-04-04?16:52??KLFDA\demo_KLFDA.m
?????文件???????2443??2009-04-04?21:12??KLFDA\KLFDA.m
?????文件????????199??2006-12-22?10:58??KLFDA\Kmatrix_Gauss.m
?????目錄??????????0??2010-04-20?09:33??KLFDA
-----------?---------??----------?-----??----
?????????????????5113????????????????????4
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