A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Vector Quantization with Training Data Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
VECIMS'09 Proceedings of the 2009 IEEE international conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems
Nonorthogonal approximate joint diagonalization with well-conditioned diagonalizers
IEEE Transactions on Neural Networks
International Journal of Information and Computer Security
A reference suite design for blind signal separation
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
An ICA learning algorithm utilizing geodesic approach
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Adaptive weighted orthogonal constrained algorithm for blind source separation
Digital Signal Processing
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From the Publisher:This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail.. "This volume will be of interest to postgraduate students and researchers in connections AI, signal processing and neural networks, as well as research and development workers, technology development engineers and research engineers.