Multi-Label Dimensionality Reduction

Liang Sun, Shuiwang Ji, Jieping Ye

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


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  • Provides a complete survey of multi-label learning and dimensionality reduction for multi-label learning
  • Describes existing dimensionality reduction algorithms, including canonical correlation analysis and partial least squares, and explores new developments of these traditional algorithms, such as the recently proposed sparse canonical correlation analysis algorithms
  • Highlights the strengths and shortcomings of many standard and new dimensionality reduction algorithms for multi-label learning in a unified framework
  • Illustrates how to apply multi-label dimensionality reduction algorithms to solve real-world problems in web page categorization, gene and protein function prediction, and other areas
  • Develops a MATLAB toolbox for the efficient implementation of the algorithms, with the package available online


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.