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, ...

The well-known error estimates for the numerical computation of eigenvalues of symmetric integral equations are extended to the computation of the eigenvectors. The ...

This is a preview. Log in through your library . Publisher Information "The Society for Industrial and Applied Mathematics is a leading international association for applied mathematics, and its ...

Network analysis begins with data that describes the set of relationships among the members of a system. The goal of analysis is to obtain from the low-level relational data a higher-level description ...

Correction: The original version of this article incorrectly stated that eigenvalues are the magnitudes of eigenvectors. In fact, eigenvalues are scalars that are multiplied with eigenvectors. This ...

The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the ...

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.

Ask your average student of mathematics/engineering/computer science about the scene, and she will practically snort with laughter. It’s a classic Hollywood attempt ...