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Poster
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
Jun-ichiro Hirayama · Aapo Hyv√§rinen · Motoaki Kawanabe

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #15

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.

Author Information

Jun-ichiro Hirayama (RIKEN AIP / ATR)
Aapo Hyvärinen (UCL)
Motoaki Kawanabe (ATR / RIKEN)

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