Plato's theory of ideas revisited
Neural Networks - 1997 special issue on neural networks for consciousness
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
Boolean Factor Analysis by Attractor Neural Network
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Learning of objects from complex patterns is a long-term challenge in philosophy, neuroscience, machine learning, data mining, and in statistics. There are some approaches in literature trying to solve this difficult task consisting in discovering hidden structure of highdimensional binary data and one of them is Boolean factor analysis. However there is no expert independent measure for evaluating this method in terms of the quality of solutions obtained, when analyzing unknown data. Here we propose information gain, model-based measure of the rate of success of individual methods. This measure presupposes that observed signals arise as Boolean superposition of base signals with noise. For the case whereby a method does not provide parameters necessary for information gain calculation we introduce the procedure for their estimation. Using an extended version of the "Bars Problem" generation of typical synthetics data for such a task, we show that our measure is sensitive to all types of data model parameters and attains its maximum, when best fit is achieved.