Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...
Mathematics of Computation, Vol. 87, No. 309 (January 2018), pp. 237-259 (23 pages) Abstract This paper is concerned with computations of a few smallest eigenvalues (in absolute value) of a large ...
This article presents a from-scratch C# implementation of the second technique: using SVD to compute eigenvalues and eigenvectors from the standardized source data. If you're not familiar with PCA, ...
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