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Type: Journal article
Title: Designing relevant features for continuous datasets using ICA
Author: Prasad, M.
Sowmya, A.
Koch, I.
Citation: International Journal of Computational Intelligence and Applications, 2008; 7(4):447-468
Publisher: Imperial College Press
Issue Date: 2008
ISSN: 1469-0268
Statement of
Mithun Prasad, Arcot Sowmya and Inge Koch
Abstract: Isolating relevant information and reducing the dimensionality of the original data set are key areas of interest in pattern recognition and machine learning. In this paper, a novel approach to reducing dimensionality of the feature space by employing independent component analysis (ICA) is introduced. While ICA is primarily a feature extraction technique, it is used here as a feature selection/construction technique in a generic way. The new technique, called feature selection based on independent component analysis (FS_ICA), efficiently builds a reduced set of features without loss in accuracy and also has a fast incremental version. When used as a first step in supervised learning, FS_ICA outperforms comparable methods in efficiency without loss of classification accuracy. For large data sets as in medical image segmentation of high-resolution computer tomography images, FS_ICA reduces dimensionality of the data set substantially and results in efficient and accurate classification.
Keywords: Feature subset selection
independent component analysis
DOI: 10.1142/S1469026808002387
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Appears in Collections:Aurora harvest 5
Mathematical Sciences publications

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