What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Modelling Brain Function: The World of Attractor Neural Networks
Modelling Brain Function: The World of Attractor Neural Networks
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Recurrent-neural-network-based Boolean factor analysis and its application to word clustering
IEEE Transactions on Neural Networks
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Boolean Factor Analysis by Attractor Neural Network
IEEE Transactions on Neural Networks
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What is suggested is a new approach to Boolean factor analysis, which is an extension of the previously proposed Boolean factor analysis method: Hopfield-like attractor neural network with increasing activity. We increased its applicability and robustness when complementing this method by a maximization of the learning set likelihood function defined according to the Noisy-OR generative model. We demonstrated the efficiency of the new method using the data set generated according to the model. Successful application of the method to the real data is shown when analyzing the data from the Kyoto Encyclopedia of Genes and Genomes database which contains full genome sequencing for 1368 organisms.