Limit search to available items
Book Cover
Author Sun, Liang, author

Title Multi-label dimensionality reduction / Liang Sun, Suiwang Ji, and Jieping Ye
Published Boca Raton, FL : CRC Press, [2013]
Online access available from:
ProQuest Ebook Central Subscription    View Resource Record  
ProQuest Ebook Central Subscription    View Resource Record  


Description 1 online resource (xi, 194 pages) : illustrations
Series Chapman & Hall/CRC machine learning & pattern recognition series
Chapman & Hall/CRC machine learning & pattern recognition series
Contents 1. Introduction -- 2. Partial least squares -- 3. Canonical correlation analysis -- 4. Hypergraph spectral learning -- 5. A scalable two-stage approach for dimensionality reduction -- 6. A shared-subspace learning framework -- 7. Joint dimensionality reduction and classification -- 8. Nonlinear dimensionality reduction : algorithms and applications
Summary 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 MATLABĀ® package for implementing popular dimensionality reduction algorithms
Bibliography Includes bibliographical references
Notes Online resource; title from PDF title page (CRCnetBASE, viewed November 22, 2013)
Subject Dimension reduction (Statistics)
Statistics as Topic
Dimension reduction (Statistics)
Form Electronic book
Author Ji, Shuiwang, author
Ye, Jieping, author
ISBN 1439806160
Other Titles Multilabel dimensionality reduction