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 (R) package for implementing popular dimensionality reduction algorithms.
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).
Introduction 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 Notations Organization 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 Background 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 References Index