Transcription of A Tutorial on Principal Component Analysis
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A Tutorial on Principal Component Analysis
A Tutorial on Principal Component AnalysisJonathon Shlens Systems Neurobiology Laboratory,Salk Insitute for Biological StudiesLa Jolla, CA 92037 andInstitute for Nonlinear Science,University of California,San DiegoLa Jolla, CA 92093-0402(Dated: December 10, 2005; Version 2) Principal Component Analysis (PCA) is a mainstay of modern data Analysis - a black box thatis widely used but poorly understood. The goal of this paper is to dispel the magic behind thisblack box. This Tutorial focuses on building a solid intuition for how and why Principal componentanalysis works; furthermore, it crystallizes this knowledge by deriving from simple intuitions, themathematics behindPCA. This Tutorial does not shy away from explaining the ideas informally,nor does it shy away from the mathematics. The hope is that by addressing both aspects, readersof all levels will be able to gain a better understanding ofPCAas well as the when, the how andthe why of applying this INTRODUCTIONP rincipal Component Analysis (PCA) has been calledone of the most valuable results from applied linear used abundantly in all forms of Analysis -from neuroscience to computer graphics - because it is asimple, non-parametric method of extracting relevant in-formation from confusing data sets.
A Tutorial on Principal Component Analysis ... set only serving to obfuscate the dynamics further. This toy example is the challenge experimenters face everyday. We will refer to this example as we delve further into ab-stract concepts. Hopefully, by the end of this paper we
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