文件名称:PCA_tutorial
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This tutorial is designed to give the reader an understanding of Principal Components
Analysis (PCA). PCA is a useful statistical technique that has found application in
fields such as face recognition and image compression, and is a common technique for
finding patterns in data of high dimension.
Before getting to a descr iption of PCA, this tutorial first introduces mathematical
concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors
and eigenvalues. This background knowledge is meant to make the PCA section
very straightforward, but can be skipped if the concepts are already familiar.
Analysis (PCA). PCA is a useful statistical technique that has found application in
fields such as face recognition and image compression, and is a common technique for
finding patterns in data of high dimension.
Before getting to a descr iption of PCA, this tutorial first introduces mathematical
concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors
and eigenvalues. This background knowledge is meant to make the PCA section
very straightforward, but can be skipped if the concepts are already familiar.
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PCA_tutorial.pdf