Lazy Learning for Nonparametric Locally Weighted Regression
Seok-Beom Roh, Yong Soo Kim, Tae-Chon Ahn · International Journal of Fuzzy Logic and Intelligent Systems · 2020
In this study, a newly designed local model called locally weighted regression model is proposed for the regression problem.This model predicts the output for a newly submitted data point.In general, the local regression model focuses on an area of the input space specified by a certain kernel function (Gaussian function, in particular).The local area is defined as a region enclosed by a neighborhood of the given query point.The weights assigned to the local area are determined by the related entries of the partition matrix originating from the fuzzy C-means method.The local regression model related to the local area is constructed using a weighted estimation technique.The model exploits the concept of the nearest neighbor, and constructs the weighted least square estimation once a new query is provided given.We validate the modeling ability of the overall model based on several numeric experiments.