LAUSR

Multilayer perceptron networks have been designed to solve supervised learning problems in which there is a set of known labeled training feature vectors. The resulting model allows us to infer adequate labels for unknown input vectors. Traditionally, the optimal model is the one that minimizes the error between the known labels and those inferred labels via such a model. The training process results in those weights that achieve the most adequate labels. Training implies a search process which is usually determined by the descent gradient of the error. In this work, we propose to replace the known labels by a set of such labels induced by a validity index. The validity index represents a measure of the adequateness of the model relative only to intrinsic structures and relationships of the set of feature vectors and not to previously known labels. Since, in general, there is no guarantee of the differentiability of such an index, we resort to heuristic optimization techniques. Our proposal results in an unsupervised learning approach for multilayer perceptron networks that allows us to infer the best model relative to labels derived from such a validity index which uncovers the hidden relationships of an unlabeled dataset.