Principal Component Analysis
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Principal Component Analysis (PCA, [24, 25]) is a technique which, quite literally, takes a di_erent viewpoint of multivariate data. In fact, PCA de_nes new variables, consisting of linear combinations of the original ones, in such a way that the _rst axis is in the direction containing most variation. Every subsequent new variable is orthogonal to previous variables, but again in the direction containing most of the remaining variation. The new variables are examples of what often is called latent variables (LVs), and in the context of PCA they are also termed principal components (PCs).
KeywordsIndependent Component Analysis Independent Component Analysis Scree Plot Principal Coordinate Analysis Factor Analysis Model
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