Efficient Feature Selection Based on Independent Component Analysis

2005 ◽  
Author(s):  
Mithun Prasad ◽  
Arcot Sowmya ◽  
Inge Koch
Keyword(s):  
Feature Selection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

Feature selection via Boolean independent component analysis

2009 ◽  
Vol 179 (22) ◽  
pp. 3815-3831 ◽  
Author(s):  
Bruno Apolloni ◽  
Simone Bassis ◽  
Andrea Brega
Keyword(s):  
Feature Selection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

DESIGNING RELEVANT FEATURES FOR CONTINUOUS DATA SETS USING ICA

2008 ◽  
Vol 07 (04) ◽  
pp. 447-468 ◽  
Author(s):  
MITHUN PRASAD ◽  
ARCOT SOWMYA ◽  
INGE KOCH
Keyword(s):  
Feature Selection ◽  
Independent Component Analysis ◽  
Large Data ◽  
Original Data ◽  
Component Analysis ◽  
Medical Image Segmentation ◽  
Independent Component ◽  
Data Sets ◽  
Construction Technique ◽  
Data Set

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.

Sign in / Sign up

Export Citation Format

Share Document