A New Hierarchical Pattern Recognition Method Using Mirroring Neural Networks

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In this paper, the authors develop a hierarchical classifier (an inverted tree-like structure) consisting of an organized set of "Blocks" each of which is actually a module that performs a feature extraction and an associated classification. They build each of such blocks by coupling a Mirroring Neural Network (MNN) with a clustering (algorithm) wherein the functions of the MNN are automatic data reduction and feature extraction which precedes an unsupervised classification. They then device an algorithm which they name as a "Tandem Algorithm" for the self-supervised learning of the MNN and an ensuing process of unsupervised pattern classification so that an ensemble of samples presented to the hierarchical classifier is classified and then sub-classified automatically.