✦ LIBER ✦

A non-linear mapping-based generalized backpropagation network for unsupervised learning

✍ Scribed by Jian-Hui Jiang; Ji-Hong Wang; Yi-Zeng Liang; Ru-Qin Yu

Publisher: John Wiley and Sons
Year: 1996
Tongue: English
Weight: 717 KB
Volume: 10
Category: Article
ISSN: 0886-9383
DOI: 10.1002/(sici)1099-128x(199605)10:3<241::aid-cem421>3.0.co;2-2

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✦ Synopsis

An unsupervised learning network is developed by incorporating the idea of non-linear mapping (NLM) into a backpropagation (BP) algorithm. This network performs the learning process by 2iteratively adjusting its network parameters to minimize an appropriate criterion using a generalized BP (GBP) algorithm. This generalization makes the BP learning algorithms more competent for many supervised and unsupervised learning tasks provided that an appropriate criterion has been designed. Results of numerical simulation and real data show that the proposed technique is a promising approach to visualize multidimensional clusters by mapping the multidimensional data to a perceivable low-dimensional space.

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## Abstract In this paper, a synthesis method developed in the last few years is applied to derive a cellular non‐linear network (CNN) able to find an approximate solution to a variational image‐fusion problem. The functional to be minimized is based on regularization theory and takes into account