Hirayama, J; Hyvarinen, AJ; Kawanabe, M; (2017) SPLICE: Fully tractable hierarchical extension of ICA with pooling. In: Precup, D and Teh, YW, (eds.) Volume 70: International Conference on Machine Learning, 6-11 August 2017, International Convention Centre,. (pp. pp. 1491-1500). Machine Learning Research: Sydney, Australia.

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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 subspace 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 electroencephalography (EEG) and natural images demonstrate the validity of the method.

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