Song, L. and Lu, H. orcid.org/0000-0002-0349-2181 (2016) EcoICA: Skewness-based ICA via Eigenvectors of Cumulant Operator. In: Proceedings of The 8th Asian Conference on Machine Learning. The 8th Asian Conference on Machine Learning, November 16-18 2016, The University of Waikato, Hamilton, New Zealand. , pp. 445-460.
Abstract
Independent component analysis (ICA) is an important unsupervised learning method. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of kurtosic sources may not always be satisfied in practice. For weak-kurtosic but skewed sources, kurtosis-based methods could fail while skewness-based methods seem more promising, where skewness is another non-Gaussianity metric measuring the nonsymmetry of signals. Partly due to the common assumption of signal symmetry, skewness-based ICA has not been systematically studied in spite of some existing works. In this paper, we take a systematic approach to develop EcoICA, a new skewness-based ICA method for weak-kurtosic but skewed sources. Specifically, we design a new cumulant operator, define its eigenvalues and eigenvectors, reveal their connections with the ICA model to formulate the EcoICA problem, and use Jacobi method to solve it. Experiments on both synthetic and real data show the superior performance of EcoICA over existing kurtosis-based and skewness-based methods for skewed sources. In particular, EcoICA is less sensitive to sample size, noise, and outlier than other methods. Studies on face recognition further confirm the usefulness of EcoICA in classification. Keywords: Independent Component Analysis, Cumulant Operator, Skewness, Eigenvectors
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Author(s). Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Independent Component Analysis; Cumulant Operator; Skewness; Eigenvectors |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Jan 2017 11:52 |
Last Modified: | 12 Jan 2017 12:02 |
Published Version: | http://www.jmlr.org/proceedings/papers/v63/ |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109947 |