Multi-Label Dimensionality Reduction: 1st Edition (Hardback) book cover

Multi-Label Dimensionality Reduction

1st Edition

By Liang Sun, Shuiwang Ji, Jieping Ye

Chapman and Hall/CRC

208 pages | 23 B/W Illus.

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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.

Table of Contents


Introduction to Multi-Label Learning

Applications of Multi-Label Learning

Challenges of Multi-Label Learning

State of the Art

Dimensionality Reduction for Multi-Label Learning

Overview of the Book



Partial Least Squares

Basic Models of Partial Least Squares

Partial Least Squares Variants

Partial Least Squares Regression

Partial Least Squares Classification

Canonical Correlation Analysis

Classical Canonical Correlation

Sparse CCA

Relationship between CCA and Partial Least Squares

The Generalized Eigenvalue Problem

Hypergraph Spectral Learning

Hypergraph Basics

Multi-Label Learning with a Hypergraph

A Class of Generalized Eigenvalue Problems

The Generalized Eigenvalue Problem versus the Least Squares Problem

Empirical Evaluation

A Scalable Two-Stage Approach for Dimensionality Reduction

The Two-Stage Approach with Regularization

Empirical Evaluation

A Shared-Subspace Learning Framework

The Framework

An Efficient Implementation

Related Work

Connections with Existing Formulations

A Feature Space Formulation

Empirical Evaluation

Joint Dimensionality Reduction and Classification


Joint Dimensionality Reduction and Multi-Label Classification

Dimensionality Reduction with Different Input Data

Empirical Evaluation

Nonlinear Dimensionality Reduction: Algorithms and Applications

Background on Kernel Methods

Kernel Centering and Projection

Kernel Canonical Correlation Analysis

Kernel Hypergraph Spectral Learning

The Generalized Eigenvalue Problem in the Kernel-Induced Feature Space

Kernel Least Squares Regression

Dimensionality Reduction and Least Squares Regression in the Feature Space

Gene Expression Pattern Image Annotation

Appendix: Proofs



About the Authors

Liang Sun is a scientist in the R&D of Opera Solutions, a leading company in big data science and predictive analytics. He received a PhD in computer science from Arizona State University. His research interests lie broadly in the areas of data mining and machine learning. His team won second place in the KDD Cup 2012 Track 2 and fifth place in the Heritage Health Prize. In 2010, he won the ACM SIGKDD best research paper honorable mention for his work on an efficient implementation for a class of dimensionality reduction algorithms.

Shuiwang Ji is an assistant professor of computer science at Old Dominion University. He received a PhD in computer science from Arizona State University. His research interests include machine learning, data mining, computational neuroscience, and bioinformatics. He received the Outstanding PhD Student Award from Arizona State University in 2010 and the Early Career Distinguished Research Award from Old Dominion University’s College of Sciences in 2012.

Jieping Ye is an associate professor of computer science and engineering at Arizona State University, where he is also the associate director for big data informatics in the Center for Evolutionary Medicine and Informatics and a core faculty member of the Biodesign Institute. He received a PhD in computer science from the University of Minnesota, Twin Cities. His research interests include machine learning, data mining, and biomedical informatics. He is an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He has won numerous awards from Arizona State University and was a recipient of an NSF CAREER Award. His papers have also been recognized at the International Conference on Machine Learning, KDD, and the SIAM International Conference on Data Mining (SDM).

About the Series

Chapman & Hall/CRC Machine Learning & Pattern Recognition

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