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Content Provider | IEEE Xplore Digital Library |
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Author | Jun Yan Ning Liu Shuicheng Yan Qiang Yang Zheng Chen |
Copyright Year | 2009 |
Abstract | Dimension Reduction (DR) algorithms are generally categorized into feature extraction and feature selection algorithms. In the past, few works have been done to contrast and unify the two algorithm categories. In this work, we introduce a matrix trace oriented optimization framework to provide a unifying view for both feature extraction and selection algorithms. We show that the unified view of DR algorithms allows us to discover some essential relationships among many state-of- the-art DR algorithms. Inspired by these essential insights, we propose to synthesize unlimited number of novel DR algorithms by combining, mapping and integrating the state- of-the-art algorithms. We present examples of newly synthesized DR algorithms with experimental results to show the effectiveness of our automatically synthesized algorithms. |
Starting Page | 598 |
Ending Page | 606 |
File Size | 434156 |
Page Count | 9 |
File Format | |
ISBN | 9781424452422 |
ISSN | 15504786 |
DOI | 10.1109/ICDM.2009.34 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-12-06 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Iron Machine learning algorithms Feature extraction Linear discriminant analysis Filtering algorithms Machine learning Principal component analysis Data mining Asia Computer science feature extraction dimension reduction feature selection |
Content Type | Text |
Resource Type | Article |
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