A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification

Details A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification

2009-11-02

arxiv.org/abs/0911.0225v1

International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 1, pp. 016-025, October 2009, USA

Computer Science

Learning

10 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact

Scientific paper

In this paper, we prove a crucial theorem called Mirroring Theorem which affirms that given a collection of samples with enough information in it such that it can be classified into classes and subclasses then (i) There exists a mapping which classifies and subclassifies these samples (ii) There exists a hierarchical classifier which can be constructed by using Mirroring Neural Networks (MNNs) in combination with a clustering algorithm that can approximate this mapping. Thus, the proof of the Mirroring theorem provides a theoretical basis for the existence and a practical feasibility of constructing hierarchical classifiers, given the maps. Our proposed Mirroring Theorem can also be considered as an extension to Kolmogrovs theorem in providing a realistic solution for unsupervised classification. The techniques we develop, are general in nature and have led to the construction of learning machines which are (i) tree like in structure, (ii) modular (iii) with each module running on a common algorithm (tandem algorithm) and (iv) selfsupervised. We have actually built the architecture, developed the tandem algorithm of such a hierarchical classifier and demonstrated it on an example problem.

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