Recently I delivered a seminar on Principal Component Analysis at University of Cincinnati, Cincinnati. Presentation is available as a PDF file. The PCA method is explained with a simple example wherein the Graduate Office has to admit 2 students out of 4 applications received based on three dimensions i.e. GRE score, GPA and the Professor Rating. Principal Component Analysis or simply PCA is a data dimension reduction technique popularly used in case of a multidimensional data. It calculates the principal components which are linear combinations of the observations in the direction of maximum variance.
Introduction to Principal Component Analysis
Recently I delivered a seminar on Principal Component Analysis at University of Cincinnati, Cincinnati. Presentation is available as a PDF file. The PCA method is explained with a simple example wherein the Graduate Office has to admit 2 students out of 4 applications received based on three dimensions i.e. GRE score, GPA and the Professor Rating. Principal Component Analysis or simply PCA is a data dimension reduction technique popularly used in case of a multidimensional data. It calculates the principal components which are linear combinations of the observations in the direction of maximum variance.
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