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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization|B. K. Tripathy

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

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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.

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Details

  • ISBN-13: 9781032041018
  • ISBN-10: 1032041013
  • Publisher: CRC Press
  • Publish Date: September 2021
  • Dimensions: 9.21 x 6.14 x 0.44 inches
  • Shipping Weight: 0.94 pounds
  • Page Count: 160

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