Principal Component Analysis from Scratch Using Singular Value Decomposition with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a classical ML technique ...

Parallel algorithms for singular value decomposition (SVD) have risen to prominence as an indispensable tool in high-performance numerical linear algebra. They offer significant improvements in the ...

We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations ...

Two-way functional data consist of a data matrix whose row and column domains are both structured, for example, temporally or spatially, as when the data are time series collected at different ...

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 ...