Name: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (en Inglés)
SKU: 55661955
Price: 322.78 USD
Availability: InStock
Author: B.K. Tripathy; Anveshrithaa Sundareswaran; Shrusti Ghela
ISBN: 9781032041018

portada Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (en Inglés)

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

B.K. Tripathy; Anveshrithaa Sundareswaran; Shrusti Ghela

Idioma

Inglés

Encuadernación

Tapa Dura

ISBN13

9781032041018

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (en Inglés)

B.k. Tripathy; Anveshrithaa Sundareswaran; Shrusti Ghela (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (en Inglés)"

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.