A Neural Network for PCA and Beyond
Neural Processing Letters
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Basic research for coloring multichannel MRI data
Proceedings of the conference on Visualization '00
Estimating Overcomplete Independent Component Bases for Image Windows
Journal of Mathematical Imaging and Vision
An Attempt for Coloring Multichannel MR Imaging Data
IEEE Transactions on Visualization and Computer Graphics
Unsupervised learning in neural computation
Theoretical Computer Science - Natural computing
PCA-Based Model Selection and Fitting for Linear Manifolds
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
The Minimum Entropy and Cumulants Based Contrast Functions for Blind Source Extraction
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Towards global principles of brain processing
Computational models for neuroscience
Adaptive blind separation with an unknown number of sources
Neural Computation
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Topographic Independent Component Analysis
Neural Computation
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
Adaptive Algorithm for Blind Separation from Noisy Time-Varying Mixtures
Neural Computation
Data-guided model combination by decomposition and aggregation
Machine Learning
Near---Far Resistant ICA based Detector for DS-CDMA Systems in the Downlink
Wireless Personal Communications: An International Journal
A histogram based data-reducing algorithm for the fixed-point independent component analysis
Pattern Recognition Letters
Overcomplete topographic independent component analysis
Neurocomputing
Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks
Neural Processing Letters
IEEE Transactions on Signal Processing
ICA color space for pattern recognition
IEEE Transactions on Neural Networks
A Neural Network Based Framework for Audio Scene Analysis in Audio Sensor Networks
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Blind source separation based on self-organizing neural network
Engineering Applications of Artificial Intelligence
Dimension reduction based on orthogonality: a decorrelation method in ICA
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
An offline independent component analysis algorithm for colored sources
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Feature analysis of mouse dynamics in identity authentication and monitoring
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
An extended online Fast-ICA algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Wavelet and Unsupervised Learning Techniques for Experimental Biomedical Data Processing
International Journal of Measurement Technologies and Instrumentation Engineering
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Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures related to multilayer feedforward networks for performing complete ICA. The basic ICA network consists of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. We consider learning algorithms for each layer, and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved. The proposed class of networks yields good results in test examples with both artificial and real-world data