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» A Constraint Learning Algorithm for Blind Source Separation
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COOPIS
2004
IEEE
13 years 11 months ago
Learning Classifiers from Semantically Heterogeneous Data
Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
Doina Caragea, Jyotishman Pathak, Vasant Honavar
ICASSP
2010
IEEE
13 years 8 months ago
Multiplicative update rules for nonnegative matrix factorization with co-occurrence constraints
Nonnegative matrix factorization (NMF) is a widely-used tool for obtaining low-rank approximations of nonnegative data such as digital images, audio signals, textual data, financ...
Steven K. Tjoa, K. J. Ray Liu
ICML
2007
IEEE
14 years 8 months ago
Nonlinear independent component analysis with minimal nonlinear distortion
Nonlinear ICA may not result in nonlinear blind source separation, since solutions to nonlinear ICA are highly non-unique. In practice, the nonlinearity in the data generation pro...
Kun Zhang, Laiwan Chan
IJON
2008
114views more  IJON 2008»
13 years 7 months ago
A robust model for spatiotemporal dependencies
Real-world data sets such as recordings from functional magnetic resonance imaging often possess both spatial and temporal structure. Here, we propose an algorithm including such ...
Fabian J. Theis, Peter Gruber, Ingo R. Keck, Elmar...
IJCNN
2007
IEEE
14 years 2 months ago
FEBAM: A Feature-Extracting Bidirectional Associative Memory
—In this paper, a new model that can ultimately create its own set of perceptual features is proposed. Using a bidirectional associative memory (BAM)-inspired architecture, the r...
Sylvain Chartier, Gyslain Giguère, Patrice ...