Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, ...

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized "eigengenes" × "eigenarrays" space, where the ...

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

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 ...

The Data Science Lab Principal Component Analysis from Scratch Using Singular Value Decomposition with C# 02/16/2024 Get Code Download Principal component analysis (PCA) is a classical machine ...