Könyv Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization Tripathy

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: 50 % esély
Keressük az egész világon
88 203 Ft
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2021
oldal
160
EAN
9781032041018
ISBN
1032041013
Enbook ID
35826629
Súly
480
Méretek
156 x 234

Teljes leírás

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.

FEATURES

  • Demonstrates how unsupervised learning approaches can be used for dimensionality reduction
  • Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts
  • Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use
  • Provides use cases, illustrative examples, and visualizations of each algorithm
  • Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis

This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.

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