Representation of Nonlinear Data Structures through Fast Vqp Neural Network
نویسنده
چکیده
The Vector Quantization and Projection neural network (VQP) is a kind of Self-Organizing Map (SOM) where neurons are not xed on an a priori deened discrete lattice, as in Kohonen maps: they nd their position in a continuous output projection space through a self-learning algorithm. The main property is therefore the ability to map arbitrary shapes of input distribution, and to project them in a non-linear way, even if the input data sub-manifold is strongly folded. This gives a useful representation of redundant data structures where Principal Components Analysis (PCA) or similar linear techniques fail to reduce to relevant subsets of parameters. Another interesting feature is that, because of a continuous learning scheme, the network remains adaptative, being able to react to an input distribution change. The rst presentation of VQP ?] was done with a simple projection learning scheme and a classical vector quantization (VQ) algorithm, which suuered from the usual slow rates of convergence of classical algorithms. After a discussion on some VQ algorithms aspects, an optimized version of VQP algorithm is presented. Mots cl es: Cartes auto-organisatrices, analyse de donn ees, projection non lin eaire, redondance de donn ees, dimensions elev ees.
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تاریخ انتشار 1993