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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Linear Dimensionality Reduction

Linear Dimensionality Reduction

Franc, Alain

Paperback

Series: Lecture Notes in Statistics, Book 228

ApplicationsDatabasesProbability & Statistics

Currently unavailable to order

ISBN10: 3031957849
ISBN13: 9783031957840
Publisher: Springer
Published: Oct 2 2025
Pages: 152
Weight: 0.59
Height: 0.39 Width: 6.20 Depth: 9.28
Language: English
This book provides an overview of some classical linear methods in Multivariate Data Analysis. This is an old domain, well established since the 1960s, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with a geometric flavor. Both approaches are tightly linked: it is easier to learn patterns from data in low-dimensional spaces than in high-dimensional ones. It is shown how a diversity of methods and tools boil down to a single core method, PCA with SVD, so that the efforts to optimize codes for analyzing massive data sets like distributed memory and task-based programming, or to improve the efficiency of algorithms like Randomized SVD, can focus on this shared core method, and benefit all methods.

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