Principal component analysis

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Principal Component Analysis, Supervised Learning, Data Visualization Tools, Data Map, Data Visualisation, Standard Deviation, Gene Expression, Learning Methods, Predictive Analytics

This formula-free summary provides a short overview about how PCA (principal component analysis) works for dimension reduction, that is, to select k features (also called variables) among a larger set of n features, with k much smaller than n. This smaller set of k features built with PCA is the best subset of k features,… Read More »Introduction to Principal Component Analysis

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The Fundamental Difference Between Principal Component Analysis and Factor Analysis Factor Analysis, Data Science Statistics, Statistics Notes, Principal Component Analysis, Data Analyst, Research Methods, Book Writing Tips, Deep Learning, Biotechnology

Principal Component Analysis and Factor Analysis are similar in many ways. They appear to be varieties of the same analysis rather than two different methods. Yet there is a fundamental difference between them that has huge effects on how to use them.

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