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)

Wednesday, 18 June 2014

Embedding of potentially asymmetric proximity data (visualization)

Published a paper with Marc Strickert and Eyke Hüllermeier about

Correlation-based embedding of pairwise score data

The approach can be applied to embed potentially asymmetric proximity (score) data preserving

correlation between the data. It also shows multiple theoretical insights into the conceptual behavior of alternative approaches.

http://www.sciencedirect.com/science/article/pii/S0925231214003956

Machine learning for non-metric proximity data

Wednesday, 18 June 2014

Embedding of potentially asymmetric proximity data (visualization)

Correlation-based embedding of pairwise score data

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