Scribe Notes: Multivariate Gaussians and PCA
نویسنده
چکیده
The idea of dimension reduction is to find a hyperplane in high-dimensional space such that an objective function [of the data with respect to its projection on the hyperplane] is optimized. In certain cases, we can interpret the dimensions that are obtained; a case study where this has worked is the ideal point model in voting. A commonly used method for dimension reduction is principal component analysis, or its more general form, factor analysis. To develop this method, we will begin by specifying a distribution over p-vectors, then describe variables in a graphical model in terms of this distribution.
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