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A non‐linear mapping‐based generalized backpropagation network for unsupervised learning
Author(s) -
Jiang JianHui,
Wang JiHong,
Liang YiZeng,
Yu RuQin
Publication year - 1996
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199605)10:3<241::aid-cem421>3.0.co;2-2
Subject(s) - backpropagation , generalization , unsupervised learning , computer science , artificial intelligence , artificial neural network , supervised learning , process (computing) , machine learning , pattern recognition (psychology) , algorithm , mathematics , mathematical analysis , operating system
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.