A NEURO-FUZZY MODEL FOR DIMENSIONALITY REDUCTION AND ITS APPLICATION
Vitaliy Kolodyazhniy, Frank Klawonn, Katharina Tschumitschew · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2007
A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high-dimensional wastewater treatment plant data.