M-ar-K-Fast Independent Component Analysis

by   Luca Parisi, PhD, MBA Candidate, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4


m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn

This paper describes the 'm-arcsinh', a modified ('m-') version of the i...

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

As an unsupervised dimensionality reduction method, principal component ...

Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem

With the popularity of Machine Learning (ML) solutions, algorithms and d...

Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction

Very often data we encounter in practice is a collection of matrices rat...

Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents

As discussed in previous studies, the efficacy of evolutionary or reinfo...

Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction

Fast and invariant feature extraction is crucial in certain computer vis...

Tensor Decompositions: A New Concept in Brain Data Analysis?

Matrix factorizations and their extensions to tensor factorizations and ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.