Combining topographic map formation with projection pursuit learning for nonparametric regression analysis
作者:Marc M. Van Hulle
摘要
The original Self-Organizing Map (SOM) algorithm is known to perform poorly on regression problems due to the occurrence of nonfunctional mappings. Recently, we have introduced an unsupervised learning rule, called the Maximum Entropy learning Rule (MER), which performs topographic map formation without using a neighborhood function. In the present paper, MER is extended with a neighborhood function and applied to nonparametric projection pursuit regression. The extended rule, called eMER, alleviates the occurrence of nonfunctional mappings. The performance of our regression procedure is quantified and compared to other neural network-based parametric and nonparametric regression procedures.
论文关键词:topographic map formation, nonparametric regression, projection pursuit learning
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论文官网地址:https://doi.org/10.1007/BF00420618