This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult ...
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This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
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Add this copy of Unsupervised Feature Extraction Applied to to cart. $187.25, new condition, Sold by discount_scientific_books rated 5.0 out of 5 stars, ships from Sterling Heights, MI, UNITED STATES, published 2024 by Springer.
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New. Print on demand Sewn binding. Cloth over boards. 533 p. Contains: Unspecified, Illustrations, black & white, Illustrations, color. Unsupervised and Semi-Supervised Learning.
Add this copy of Unsupervised Feature Extraction Applied to to cart. $262.11, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2024 by Springer.
