Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach 2020 Edition Contributor(s): Taguchi, Y-H (Author) |
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ISBN: 3030224554 ISBN-13: 9783030224554 Publisher: Springer OUR PRICE: $170.99 Product Type: Hardcover - Other Formats Published: September 2019 |
Additional Information |
BISAC Categories: - Technology & Engineering | Telecommunications - Computers | Computer Science - Technology & Engineering | Electronics - General |
Dewey: 006.312 |
Series: Unsupervised and Semi-Supervised Learning |
Physical Information: 0.81" H x 6.14" W x 9.21" (1.44 lbs) 321 pages |
Descriptions, Reviews, Etc. |
Publisher Description: This 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 tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
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