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)

Thursday 27 November 2014

Benchmark sets for indefinite proximity learning

At the page Benchmark I have provided a matlab file with 13 proximity benchmarks
at various complexity, scale and from different application domains. The page contains
also a description and references to the original work behind the datasets.