Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining

by Zheng Alan Zhao, Huan Liu
Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining

by Zheng Alan Zhao, Huan Liu

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Overview

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise

Product Details

ISBN-13: 9781000023077
Publisher: CRC Press
Publication date: 12/14/2011
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Sold by: Barnes & Noble
Format: eBook
Pages: 224
File size: 7 MB

About the Author

Zheng Zhao is a research statistician at the SAS Institute, Inc. His recent research focuses on designing and developing novel analytic approaches for handling large-scale data of extremely high dimensionality. Dr. Zhao is the author of PROC HPREDUCE, which is a SAS High Performance Analytics procedure for large-scale parallel variable selection. He was co-chair of the 2010 PAKDD Workshop on Feature Selection in Data Mining. He earned a Ph.D. in computer science and engineering from Arizona State University.

Huan Liu is a professor of computer science and engineering at Arizona State University. Dr. Liu serves on journal editorial boards and conference program committees and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He earned a Ph.D. in computer science from the University of Southern California. With a focus on data mining, machine learning, social computing, and artificial intelligence, his research investigates problems in real-world application with high-dimensional data of disparate forms, such as social media, group interaction and modeling, data preprocessing, and text/web mining.

Table of Contents

Data of High Dimensionality and Challenges. Univariate Formulations for Spectral Feature Selection. Multivariate Formulations. Connections to Existing Algorithms. Large-Scale Spectral Feature Selection. Multi-Source Spectral Feature Selection. References. Index.
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