Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

By Anveshrithaa S, B K Tripathy, Shrusti Ghela, et al.

Book Overview

This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well.

• Format: Paperback
• Language: English
• ISBN: 103204103X
• ISBN13: 9781032041032
• Release Date: September 2023
• Publisher: CRC Press
• Length: 160 Pages
• Weight: 0.56 lbs.
• Dimensions: 0.4" x 6.1" x 9.2"