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2006 NIPS NeurIPS 2006

Recursive ICA

Abstract

Independent Component Analysis (ICA) is a popular method for extracting independent features from visual data. However, as a fundamentally linear technique, there is always nonlinear residual redundancy that is not captured by ICA. Hence there have been many attempts to try to create a hierarchical version of ICA, but so far none of the approaches have a natural way to apply them more than once. Here we show that there is a relatively simple technique that transforms the absolute values of the outputs of a previous application of ICA into a normal distribution, to which ICA maybe applied again. This results in a recursive ICA algorithm that may be applied any number of times in order to extract higher order structure from previous layers.

🚀 Conference Pioneer — NIPS 2006
🧭 Keyword Pioneer — recursive independent component analysis
🐣 Hot Topic Early Bird — unsupervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
📈 Trend Setter — Unsupervised Learning

Authors

Honghao Shan , Lingyun Zhang , Garrison W. Cottrell

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Unsupervised Learning Deep Learning > Architectures > Neural Networks Machine Learning > Core Methods > Feature Learning Machine Learning > Learning Paradigms > Unsupervised Learning

Keywords

unsupervised learning blind source separation independent component analysis feature extraction recursive independent component analysis recursive ica hierarchical representation higher order structure hierarchical model recursive algorithm higher-order structure higher order statistics
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