ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation
- 1 January 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 22 (10), 1078-1089
- https://doi.org/10.1109/34.879789
Abstract
No abstract availableThis publication has 21 references indexed in Scilit:
- Learning Overcomplete RepresentationsNeural Computation, 2000
- Probabilistic framework for the adaptation and comparison of image codesJournal of the Optical Society of America A, 1999
- Independent Factor AnalysisNeural Computation, 1999
- Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian SourcesNeural Computation, 1999
- Blind signal separation: statistical principlesProceedings of the IEEE, 1998
- Natural Gradient Works Efficiently in LearningNeural Computation, 1998
- The “independent components” of natural scenes are edge filtersVision Research, 1997
- Blind source separation-semiparametric statistical approachIEEE Transactions on Signal Processing, 1997
- Infomax and maximum likelihood for blind source separationIEEE Signal Processing Letters, 1997
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995