# M-ar-K-Fast Independent Component Analysis

arXiv: https://arxiv.org/abs/2108.07908 · HTML: https://mlsanity.com/paper/2108.07908/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research](https://mlsanity.com/paper/2010.15581/) (2020)
- [Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware](https://mlsanity.com/paper/1901.09181/) (2019)
- [SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems](https://mlsanity.com/paper/1903.03129/) (2019)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics](https://mlsanity.com/paper/2010.08031/) (2020)
- [m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn](https://mlsanity.com/paper/2009.07530/) (2020)
- [hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras](https://mlsanity.com/paper/2011.07661/) (2020)

## Abstract (arXiv)

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.