Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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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
Dimensionality Reduction and Feature Selection

Dimensionality Reduction and Feature Selection

Bouchene, Mohammed Mehdi

Paperback

Technology & Engineering

ISBN10: 663010109X
ISBN13: 9786630101096
Publisher: LAP Lambert Academic Publishing
Published: Jun 30 2026
Pages: 116
Weight: 0.36
Height: 0.28 Width: 6.00 Depth: 9.00
Language: English
This book, titled A Comprehensive Guide to Dimensionality Reduction and Feature Selection by Bouchene Mohammed Mehdi, presents a structured mathematical and practical approach to managing high-dimensional data using Python.The book is organized into three primary sections:1. Foundations: Analyzes the geometric and statistical challenges of high-dimensional spaces (the Curse of Dimensionality), establishes mathematical preliminaries in linear algebra and information theory, and details classical linear projection methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).2. Manifold Learning: Explores non-linear dimensionality reduction techniques designed to preserve local and global structures in curved data manifolds, covering Isomap, Locally Linear Embedding (LLE), t-SNE, and UMAP.3. Feature Selection: Examines methodologies to isolate relevant variables, systematically categorizing them into filter, wrapper, and embedded methods (such as Lasso and tree-based techniques).The book concludes with a practical MLOps workflow roadmap and mathematical derivations.

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