資源簡介
使用LDA(線性判別分析)算法提取一維數字信號(數組)的特征,可用于信號的分類識別。

代碼片段和文件信息
function?[V_selectFV1D1]?=?TYZmyLDA(MatdogMathum)
[rowdcold]=size(Matdog);
[rowhcolh]=size(Mathum);
n=rowd+rowh;
MeanDog=mean(Matdog);
MeanHum=mean(Mathum);
Xdog=Matdog-repmat(MeanDogrowd1);
Xhum=Mathum-repmat(MeanDogrowh1);
%狗的類內散度矩陣
for?i=1:rowd
SwDog=+(Xdog(i:))‘*Xdog(i:);
end
%人的類內散度矩陣
for?j=1:rowh
SwHum=+(Xhum(j:))‘*Xhum(j:);
end
Sw=(SwDog+SwHum);%類內散布矩陣
%下面計算類間散布矩陣Sb
Mat=[Matdog;Mathum];
MeanMat=mean(Mat);
D11=MeanDog-MeanMat;
D22=MeanHum-MeanMat;
Sb=(rowd*D11‘*D11+rowh*D22‘*D22);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%相位使用1025
Sbw=Sb-Sw+eye(1025);%%%%%%%%%%避免矩陣奇異,出現復數特征值
[V1?D1]?=?eigs(Sbw);
V_select=abs(V1(:4:6));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%頻譜使用411-5HZ的特征
%?Sbw=Sb-Sw+eye(41);%%%%%%%%%%避免矩陣奇異,出現復數特征值
%?[V1?D1]?=?eigs(Sbw25);
%?V_select=abs(V1);
%%%%%%%%%%%%%%%%%%
V=V1;
D=D1;
F=Mat*V_select;
%%%%%%%%%%%%%%%%%%%%%%%----end----%%%%%%%%%%%%%%%%%%%%%%%%%%%
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件???????1047??2013-07-10?17:28??TYZmyLDA.m
-----------?---------??----------?-----??----
?????????????????1047????????????????????1
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