Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

By:

Tripathy B K

Contributor(s):

Publication details: CRC Press 2022 LondonEdition: 1stDescription: 174ISBN:

9781032600017

Subject(s):

Data Visualization

DDC classification:

005.74015 TRI

Summary: 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. Source: https://www.amazon.in/Unsupervised-Approaches-Dimensionality-Reduction-Visualization/dp/1032600012/ref=sr_1_1?crid=35NCHQAC339KZ&dib=eyJ2IjoiMSJ9.p36Dw8I5fHE6a-EbHz6AaA.Slw-NHAYgRH9kSPr4GwyWe6O1J0hcrbq0MiU9HX5HIo&dib_tag=se&keywords=9781032600017&qid=1709539928&sprefix=thinking+visually%2Caps%2C227&sr=8-1

List(s) this item appears in: New Arrivals March 2024

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Book	Main Library Analytics	Reference book	005.74015 TRI (Browse shelf(Opens below))	Available		119240

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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.

Source: https://www.amazon.in/Unsupervised-Approaches-Dimensionality-Reduction-Visualization/dp/1032600012/ref=sr_1_1?crid=35NCHQAC339KZ&dib=eyJ2IjoiMSJ9.p36Dw8I5fHE6a-EbHz6AaA.Slw-NHAYgRH9kSPr4GwyWe6O1J0hcrbq0MiU9HX5HIo&dib_tag=se&keywords=9781032600017&qid=1709539928&sprefix=thinking+visually%2Caps%2C227&sr=8-1

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