Multi-Label Dimensionality Reduction Book uri icon


  • 2014 by Taylor & Francis Group, LLC. All rights reserved. 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:

author list (cited authors)

  • Sun, L., Ji, S., & Ye, J.

complete list of authors

  • Sun, L||Ji, S||Ye, J

publication date

  • April 2016