Principal component analysis applied to IR data compression

1 Principal component analysis applied to IR data compression 

The most efficient method of data set compression in the joint basis is Principal Component Analysis (PCA). Principal Components (PCs) are constructed as a linear combination of original variables to maximize the description of data variance. They are eigenvectors of the auto-covariance matrix of data set. Each eigenvector is associated with the corresponding eigenvalue, which describes its importance in data variance description. For the studied IR library, 57 eigenvectors (principal components) are necessary to describe 95% of data variance, whereas as much as 109 eigenvectors are needed to describe 99% of data variance (see Fig.5). The mean value of RMS 

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