M-ar-K-Fast Independent Component Analysis

TL;DR

This paper introduces the m-ar-K-FastICA method, combining a new kernel with FastICA to improve feature extraction reliability and efficiency in high-entropy, uncertain data, validated across multiple datasets.

Contribution

The study develops a novel m-ar-K kernel integrated with FastICA, enhancing feature extraction in noisy and entropic data environments, and provides open-source implementation.

Findings

Improved classification performance with m-ar-K-FastICA.

Higher reliability and computational efficiency over standard FastICA.

Effective in datasets with high entropy and randomness.

Abstract

This study presents the m-arcsinh Kernel ('m-ar-K') Fast Independent Component Analysis ('FastICA') method ('m-ar-K-FastICA') for feature extraction. The kernel trick has enabled dimensionality reduction techniques to capture a higher extent of non-linearity in the data; however, reproducible, open-source kernels to aid with feature extraction are still limited and may not be reliable when projecting features from entropic data. The m-ar-K function, freely available in Python and compatible with its open-source library 'scikit-learn', is hereby coupled with FastICA to achieve more reliable feature extraction in presence of a high extent of randomness in the data, reducing the need for pre-whitening. Different classification tasks were considered, as related to five (N = 5) open access datasets of various degrees of information entropy, available from scikit-learn and the University California Irvine (UCI) Machine Learning repository. Experimental results demonstrate improvements in the classification performance brought by the proposed feature extraction. The novel m-ar-K-FastICA dimensionality reduction approach is compared to the 'FastICA' gold standard method, supporting its higher reliability and computational efficiency, regardless of the underlying uncertainty in the data.