Machine learning for non-metric proximity data

This blog provides literature, algorithms and data sets for the analysis of (indefinite) proximity data. In machine learning kernels are given as proximity data. But if the proximity measure is non-metric most kernel approaches are inaccurate or fail. This blog shows ways how to deal with these so called indefinite, non-positive or non-psd proximity data, providing links to literature and algorithms. The final objective is to provide - Probabilistic Models in Pseudo-Euclidean Spaces (ProMoS)

Friday, 1 July 2022

Sparsification of core set models in non-metric supervised learning

We sparsified our original proposal of an indefinite core vector machine.

Fast and sparse - check it out

Frank-Michael Schleif, Christoph Raab, Peter Tiño: Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. 129: 1-7 (2020)

Machine learning for non-metric proximity data

Friday, 1 July 2022

Sparsification of core set models in non-metric supervised learning

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