Descripci�n Condici�n: New. New! This book is in the same immaculate condition as when it was published 0.71. N� de ref. del art�culo: 353-103204103X-new

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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

B.K. Tripathy

Publicado por CRC Press (2023)

ISBN 10: 103204103X ISBN 13: 9781032041032

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Descripci�n PAP. Condici�n: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N� de ref. del art�culo: L0-9781032041032

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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

Tripathy, B. K./ Sundareswaran, Anveshrithaa/ Ghela, Shrusti

Publicado por CRC Pr I Llc (2023)

ISBN 10: 103204103X ISBN 13: 9781032041032

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Descripci�n Paperback. Condici�n: Brand New. 174 pages. 9.19x6.13x0.40 inches. In Stock. N� de ref. del art�culo: __103204103X

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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (Paperback)

B.K. Tripathy

Publicado por Taylor & Francis Ltd, London (2023)

ISBN 10: 103204103X ISBN 13: 9781032041032

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Descripci�n Paperback. Condici�n: new. Paperback. 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 analysisThis 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de ref. del art�culo: 9781032041032

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