Designing relevant features for continuous data sets using ICA

Mithun Prasad, Arcot Sowmya, Inge Koch

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)447-468
Number of pages22
JournalInternational Journal of Computational Intelligence and Applications
Issue number4
Publication statusPublished - 1 Dec 2008
Externally publishedYes


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