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)
