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Principal Component Analysis - Columbia University

Principal Component AnalysisFrank WoodDecember 8, 2009 This lecture borrows andquotesfrom Joliffe s Principle Component Analysis book. Go buy it! Principal Component AnalysisThe central idea of Principal Component Analysis (PCA) isto reduce the dimensionality of a data set consisting of alarge number of interrelated variables, while retaining asmuch as possible of the variation present in the data is achieved by transforming to a new set of variables,the Principal components (PCs), which are uncorrelated,and which are ordered so that the firstfewretain most ofthe variation present inallof the original variables.

matrix is to utilize the singular value decomposition of S = A0A where A is a matrix consisting of the eigenvectors of S and is a diagonal matrix whose diagonal elements are the eigenvalues corresponding to each eigenvector. Creating a reduced dimensionality projection of X is accomplished by selecting the q largest eigenvalues in and retaining ...


  Analysis, Value, Principal component analysis, Principal, Component, Singular, Decomposition, Singular value decomposition




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