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).
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia |
| Editors | Doina Precup, Yee Whye Teh |
| Number of pages | 12 |
| Publisher | International Machine Learning Society (IMLS) |
| Publication date | 2017 |
| Pages | 2351-2362 |
| ISBN (Electronic) | 9781510855144 |
| Publication status | Published - 2017 |
| MoE publication type | A4 Article in conference proceedings |
| Event | International Conference on Machine Learning - Sydney, Australia Duration: 6 Aug 2017 → 11 Aug 2017 Conference number: 34 https://icml.cc/Conferences/2017 |
Publication series
| Name | Proceedings of Machine Learning Research |
|---|---|
| Volume | 70 |
| ISSN (Electronic) | 2640-3498 |
Fields of Science
- 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