Multi-Label Dimensionality Reduction

Liang Sun, Shuiwang Ji, Jieping Ye

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English

Chapman & Hall/CRC
04 November 2013

Economic statistics; Computing: general; Databases; Data mining; Pattern recognition

Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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.

By: Liang Sun, Shuiwang Ji, Jieping Ye
Imprint: Chapman & Hall/CRC
Country of Publication: United States
Dimensions: Height: 234mm, Width: 156mm,
Weight: 476g
ISBN: 9781439806159
ISBN 10: 1439806152
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Pages: 208
Publication Date: 04 November 2013
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Hardback
Publisher's Status: Active

Introduction. Partial Least Squares. Canonical Correlation Analysis. Hypergraph Spectral Learning. A Scalable Two-Stage Approach for Dimensionality Reduction. A Shared-Subspace Learning Framework. Joint Dimensionality Reduction and Classification. Nonlinear Dimensionality Reduction: Algorithms and Applications. Appendix. References. Index.

Jieping Ye, Shuiwang Ji, and Liang Sun work in the Department of Computer Science and Engineering at Arizona State University.