文件名称:pca
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- 上传时间：2013-07-09
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Function to perform Principle Component Analysis over a set of training
vectors passed as a concatenated matrix.
Usage:- [V,D,M] = pca(X,n)
[V,D] = pca(X,aM,n)
where:-
<input>
X = concatenated set of column vectors
aM = assume that the mean is aM
n = number of principal components to extract (optional)
<output>
V = ensemble of column eigen-vectors
D = vector of eigen-values
M = mean of X (optional)
- Function to perform Principle Component Analysis over a set of training
vectors passed as a concatenated matrix.
Usage:- [V,D,M] = pca(X,n)
[V,D] = pca(X,aM,n)
where:-
<input>
X = concatenated set of column vectors
aM = assume that the mean is aM
n = number of principal components to extract (optional)
<output>
V = ensemble of column eigen-vectors
D = vector of eigen-values
M = mean of X (optional)
vectors passed as a concatenated matrix.
Usage:- [V,D,M] = pca(X,n)
[V,D] = pca(X,aM,n)
where:-
<input>
X = concatenated set of column vectors
aM = assume that the mean is aM
n = number of principal components to extract (optional)
<output>
V = ensemble of column eigen-vectors
D = vector of eigen-values
M = mean of X (optional)
- Function to perform Principle Component Analysis over a set of training
vectors passed as a concatenated matrix.
Usage:- [V,D,M] = pca(X,n)
[V,D] = pca(X,aM,n)
where:-
<input>
X = concatenated set of column vectors
aM = assume that the mean is aM
n = number of principal components to extract (optional)
<output>
V = ensemble of column eigen-vectors
D = vector of eigen-values
M = mean of X (optional)
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