DocumentCode :
827099
Title :
Feature extraction based on ICA for binary classification problems
Author :
Kwak, Nojun ; Choi, Chong-Ho
Author_Institution :
Samsung Electron., Suwon, South Korea
Volume :
15
Issue :
6
fYear :
2003
Firstpage :
1374
Lastpage :
1388
Abstract :
In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be appended with binary class labels to produce a number of features that do not carry information about the class labels-these features will be discarded-and a number of features that do. We also provide a local stability analysis of the proposed algorithm. The advantage is that general ICA algorithms become available to a task of feature extraction for classification problems by maximizing the joint mutual information between class labels and new features, although only for two-class problems. Using the new features, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.
Keywords :
feature extraction; independent component analysis; learning (artificial intelligence); numerical stability; pattern classification; algorithms; binary class labels; binary classification problems; dimension reduction; feature extraction; feature space; independent component analysis; joint mutual information; local stability analysis; supervised learning; two-class problems; Data mining; Feature extraction; Independent component analysis; Mutual information; Neural networks; Principal component analysis; Signal processing algorithms; Supervised learning; Support vector machine classification; Support vector machines;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2003.1245279
Filename :
1245279
Link To Document :
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