Multi-Label Dimensionality Reduction

1st Edition

Liang Sun, Shuiwang Ji, Jieping Ye

Chapman and Hall/CRC
Published November 4, 2013
Reference - 208 Pages - 23 B/W Illustrations
ISBN 9781439806159 - CAT# K10304
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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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 reduction
  • How to scale dimensionality reduction algorithms to large-scale problems
  • How to effectively combine dimensionality reduction with classification
  • How to derive sparse dimensionality reduction algorithms to enhance model interpretability
  • How to perform multi-label dimensionality reduction effectively in practical applications

The 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® package for implementing popular dimensionality reduction algorithms.


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