Multimodal independent component analysis?A method of feature extraction from multiple information sources
| Autor: | Shinji Umeyama, Shotaro Akaho |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Modality (human–computer interaction)
business.industry Gaussian Feature extraction Pattern recognition Function (mathematics) Mutual information Independent component analysis symbols.namesake symbols Artificial intelligence Electrical and Electronic Engineering business Gradient descent Canonical correlation Mathematics |
| Zdroj: | Electronics and Communications in Japan (Part III: Fundamental Electronic Science). 84:21-28 |
| ISSN: | 1520-6440 1042-0967 |
| DOI: | 10.1002/ecjc.1045 |
| Popis: | We propose a method to extract features from a pair of multivariate information sources. CCA (canonical correlation analysis) is a traditional method for this purpose, but it does not always succeed in producing features that are nonlinearly related, because of a Gaussian assumption. We extend the framework of CCA by introducing a criterion used in ICA (independent component analysis). The proposed framework MICA (multimodal ICA) maximizes mutual information between a pair of extracted features from the two modalities, with the features extracted from each modality being statistically independent. The cost function is given by a weighted sum of the two criteria, and it is approximated by Gram–Charlier expansion. The gradient descent learning algorithm is derived to optimize this approximated function by taking a natural gradient. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(11): 21–28, 2001 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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