Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis

摘要

This paper investigates the mathematically appropriate treatment of data density estimators in machine learning approaches, if these estimators rely on data dissimilarity density models. We show exemplarily for two well-known machine learning approaches for classification and data visualization that this dependence is apparently analyzing the respective mathematical models. We show by numerical experiments that data sets generate different data dissimilarity densities depending on the dissimilarity measure in use. Thus an appropriate choice in machine learning models is mandatory to process the data consistently.