A multiple cause mixture model for unsupervised learning
Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
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
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
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
An associative sparse coding neural network and applications
Neurocomputing
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Boolean Factor Analysis by Attractor Neural Network
IEEE Transactions on Neural Networks
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Methods for the discovery of hidden structures of high-dimensional binary data are one of the most important challenges facing the community of machine learning researchers. There are many approaches in the literature that try to solve this hitherto rather ill-defined task. In the present study, we propose a general generative model of binary data for Boolean Factor Analysis and introduce two new Expectation-Maximization Boolean Factor Analysis algorithms which maximize the likelihood of a Boolean Factor Analysis solution. To show the maturity of our solutions we propose an informational measure of Boolean Factor Analysis efficiency. Using the so-called bars problem benchmark, we compare the efficiencies of the proposed algorithms to that of Dendritic Inhibition Neural Network, Maximal Causes Analysis, and Boolean Matrix Factorization. Last mentioned methods were taken as related methods as they are supposed to be the most efficient in bars problem benchmark. Then we discuss the peculiarities of the two methods we proposed and the three related methods in performing Boolean Factor Analysis.