Neural network learning algorithms combining Kohonen's self-organizing map and Oja's principal component type learning rule are studied for data compression and estimation of the tangent spaces of the feature manifold. The approach can also be thought as a combination of vector quantization and transform coding. The algorithms are derived from certain optimization criteria leading to the local analysis of the data. A novel application for clustering and classification of overlapping classes is presented. Simulations justify the performance of the algorithms.
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