SPLICE : Fully tractable hierarchical extension of ICA with pooling

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http://hdl.handle.net/10138/307233

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Hirayama , J , Hyvärinen , A J & Kawanabe , M 2017 , SPLICE : Fully tractable hierarchical extension of ICA with pooling . in D Precup & Y W Teh (eds) , Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia . Proceedings of Machine Learning Research , vol. 70 , International Machine Learning Society (IMLS) , pp. 2351-2362 , International Conference on Machine Learning , Sydney , Australia , 06/08/2017 .

Title: SPLICE : Fully tractable hierarchical extension of ICA with pooling
Author: Hirayama, Junichiro; Hyvärinen, Aapo Johannes; Kawanabe, Motoaki
Editor: Precup, Doina; Teh, Yee Whye
Contributor: University of Helsinki, Doctoral Programme in Computer Science
Publisher: International Machine Learning Society (IMLS)
Date: 2017
Language: eng
Number of pages: 12
Belongs to series: Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia
Belongs to series: Proceedings of Machine Learning Research
ISBN: 9781510855144
URI: http://hdl.handle.net/10138/307233
Abstract: We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling. The framework incorporates a general sub-space pooling with linear ICA-like layers stacked recursively. Unlike related previous models, our generative model is fully tractable: both the likelihood and the posterior estimates of latent variables can readily be computed with analytically simple formulae. The model is particularly simple in the case of complex-valued data since the pooling can be reduced to taking the modulus of complex numbers. Experiments on elec-troencephalography (EEG) and natural images demonstrate the validity of the method. Copyright 2017 by the author(s).
Subject: Artificial intelligence
Learning systems
Neurophysiology, Analysis and modeling
Complex-valued
Generative model
Independent component analyses (ICA)
Latent variable
Modulus of complex numbers
Natural images
Probabilistic framework, Independent component analysis
113 Computer and information sciences
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