Unsupervised Feature Extraction Applied to Bioinformatics

A PCA Based and TD Based Approach

By: Y-h. Taguchi

Write A Review

eText | 23 August 2019

At a Glance

Format
ePUB

eText

$249.00

or 4 interest-free payments of $62.25 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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.

Allows readers to analyze data sets with small samples and many features;
Provides a fast algorithm, based upon linear algebra, to analyze big data;
Includes several applications to multi-view data analyses, with a focus on bioinformatics.