Multi-label Dimensionality Reduction

Jieping Ye

ISBN 10: 1439806152 / ISBN 13: 9781439806159

Published by Taylor & Francis Aug 2011, 2011

About this Item

Neuware - A comprehensive reference for researchers in machine learning, data mining, and computer vision, this book presents in-depth, systematic discussions on algorithms and applications for dimensionality reduction. It covers emerging models for general dimensionality reduction in multi-label classification. The book also presents a novel framework to unify a variety of models. Based on the discussions of the models and theory, the authors provide thorough analysis and comparison of the algorithms used in these models. They also include applications of these models and algorithms in bioinformatics and biomedical informatics. A supporting website provides updated information.

Seller Inventory # 9781439806159

Bibliographic Details

• Title: Multi-label Dimensionality Reduction
• Publisher: Taylor & Francis Aug 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 1439806152
• ISBN 13: 9781439806159
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 156H x 23D millimeters 235 millimeters width by 156 millimeters height by 23 millimeters depth
• Weight: 474 grams

About this title

Synopsis

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.

About the Author

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).