A general procedure for learning mixtures of independent component analyzers

DOI10.1016/J.PATCOG.2009.05.013zbMath1191.68601DBLPjournals/pr/SalazarVSI10OpenAlexW2026538104WikidataQ57572699 ScholiaQ57572699MaRDI QIDQ733139

Jorge Igual, Addisson Salazar, Arturo Serrano, Luis Vergara

Publication date: 15 October 2009

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2009.05.013

zbMATH Keywords

BSSsemi-supervised learningnon-parametric density estimationICAICA mixture model

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (2)

Clustering via finite nonparametric ICA mixture models ⋮ On including sequential dependence in ICA mixture models

Uses Software

• FastICA
• ICALAB

Cites Work

• Blind separation of sources that have spatiotemporal variance dependencies
• Independent component analysis, a new concept?
• Text classification from labeled and unlabeled documents using EM
• Nonparametric independent component analysis
• Efficient independent component analysis
• Topographic Independent Component Analysis
• 10.1162/153244303768966085
• 10.1162/153244303768966120
• Exploring Latent Structure of Mixture ICA Models by the Minimum β-Divergence Method
• Variational Mixture of Bayesian Independent Component Analyzers
• HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels
• An on-line ICA-mixture-model-based self-constructing fuzzy neural network
• 10.1162/jmlr.2003.4.7-8.1205
• 10.1162/jmlr.2003.4.7-8.1271
• An ICA approach to semi‐blind identification of strictly proper systems based on interactor polynomial matrix
• Consistent independent component analysis and prewhitening
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