Feature Extraction with Weighted Samples Based on Independent Component Analysis

Abstract

This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers. The method is a weighted version of our previous work based on the independent component analysis (ICA). In our previous work, ICA was applied to feature extraction for classification problems by including class information in the training. The resulting features contain much information on the class labels producing good classification performances. However, in many real world classification problems, it is hard to get a clean dataset and inherently, there may exist outliers or dubious data to complicate the learning process resulting in higher rates of misclassification. In addition, it is not unusual to find the samples with the same inputs to have different class labels. In this paper, Parzen window is used to estimate the correctness of the class information of a sample and the resulting class information is used for feature extraction.