A multiple cause mixture model for unsupervised learning
Neural Computation
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
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Competition and multiple cause models
Neural Computation
Towards a methodology to search for near-optimal representations in classification problems
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Feature discovery in classification problems
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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This paper presents a new framework extending previous work on multiple cause mixture models. We search for an optimal neural network codification of a given set of input patterns, which implies hidden cause extraction and redundancy elimination leading to a factorial code. We propose a new entropy measure whose maximization leads to both maximum information transmission and independence of internal representations for factorial input spaces in the absence of noise. No extra assumptions are needed, in contrast with previous models in which some information about the input space, such as the number of generators, must be known a priori.