This paper introduces a new framework for classifying probability density functions. The proposed method fits in the class of constrained Gaussian processes indexed by distribution functions. Firstly, instead of classifying observations directly, we consider their isometric transformations which enables us to satisfy both positiveness and unit integral hard constraints. Secondly, we introduce the theoretical proprieties and give numerical details of how to decompose each transformed observation in an appropriate orthonormal basis. As a result, we show that the coefficients are belonging to the unit sphere when equipped with the standard Euclidean metric as a natural metric. Lastly, the proposed methods are illustrated and successfully evaluated in different configurations and with various dataset.
CITATION STYLE
Fradi, A., & Samir, C. (2023). A New Framework for Classifying Probability Density Functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14169 LNAI, pp. 507–522). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43412-9_30
Mendeley helps you to discover research relevant for your work.