Multi-Label Dimensionality Reduction, Hardback Book

Description

Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality.

An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information.

The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.

Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms.

It explores numerous research questions, including: How to fully exploit label correlations for effective dimensionality reductionHow to scale dimensionality reduction algorithms to large-scale problemsHow to effectively combine dimensionality reduction with classificationHow to derive sparse dimensionality reduction algorithms to enhance model interpretabilityHow to perform multi-label dimensionality reduction effectively in practical applicationsThe authors emphasize their extensive work on dimensionality reduction for multi-label learning.

Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems.

A supplementary website provides a MATLAB (R) package for implementing popular dimensionality reduction algorithms.

Information

  • Format: Hardback
  • Pages: 208 pages, 14 Tables, black and white; 23 Illustrations, black and white
  • Publisher: Taylor & Francis Ltd
  • Publication Date:
  • Category: Data mining
  • ISBN: 9781439806159

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