Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis

Recent years have witnessed intense development of randomized methods for low-rank approximation. These methods target principal component analysis and the calculation of truncated singular value decompositions. The present article presents an essentially black-box, foolproof implementation for Math...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:ACM transactions on mathematical software Ročník 43; číslo 3
Hlavní autoři: Li, Huamin, Linderman, George C, Szlam, Arthur, Stanton, Kelly P, Kluger, Yuval, Tygert, Mark
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.01.2017
Témata:
SVD
Algorithms
Performance
singular value decomposition
PCA
Principal component analysis
ISSN:0098-3500
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Recent years have witnessed intense development of randomized methods for low-rank approximation. These methods target principal component analysis and the calculation of truncated singular value decompositions. The present article presents an essentially black-box, foolproof implementation for Mathworks' MATLAB, a popular software platform for numerical computation. As illustrated via several tests, the randomized algorithms for low-rank approximation outperform or at least match the classical deterministic techniques (such as Lanczos iterations run to convergence) in basically all respects: accuracy, computational efficiency (both speed and memory usage), ease-of-use, parallelizability, and reliability. However, the classical procedures remain the methods of choice for estimating spectral norms and are far superior for calculating the least singular values and corresponding singular vectors (or singular subspaces).
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-3500
DOI:10.1145/3004053