A Functional Data Classification Model Utilizing Functional Mahalanobis Distance and Regenerative Kernel Methods
Abstract:The classification of functional data is an important research direction in modern data mining. In this paper, we propose a similarity measurement method for functional data based on functional Mahalanobis distance and regenerative kernel theory, considering the scenario where the predictor variable is a random function and the response variable is a categorical scalar. This method is then applied to functional kernel principal component analysis. During the classification phase, classic algorithms such as sup… Show more
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