Principal components analysis history

The massive surveys both ground based as well as from space missions provide large number of stellar spectra covering distant components of Galaxy. To understand the complex evolutionary history of our Galaxy, rapid and accurate methods of stellar classification are necessary. A short review of the automated procedures are presented here. The most commonly used automated spectral classification methods are based on (a) Minimum Distance Method (MDM) (b) Gaussian Probability Method (GPM) (c) Principal Component Analysis (PCA) and (d) Artificial Neural Network (ANN). We chose to describe only two of them to introduce the automated approach of classification. 

Principal component analysis (PCA) is a statistical technique with a long history in multivariate data analysis (see Chemometrics Multivariate View on Chemical Problems) PCA reduces a set of partially cross-correlated data into a smaller set of orthogonal variables (principal components) without a significant loss in the contribution to variation. In effect, the method detects and combines descriptors which behave in a similar way into a new set of variables that are non-correlated, i.e., they are orthogonal. 

Esbensen KH, Geladi P. Principal component analysis concept, geometrical interpretation, mathematical background, algorithms, history, practice. In Brown SD, Tauler R, Walczak B, editors. Comprehensive chemometrics chemical and biochemical data analysis, vol. 2. Amsterdam Elsevier Ltd. 2009. p. 211-27 [chapter 2.13]. 

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