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)

Saturday, 6 September 2014

journal publication on conformal prediction for proximity data

The paper Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data
published at Pattern Recognition is now online. We propose a conformal classifier for
multi-class semi-supervised learning of proximity data.
Update: The article won the College best publication award of the University of Birmingham College of Engineering and Physical Sciences

Publication at ICANN 2014

Accepted paper at ICANN 2014 about Discriminative Fast Soft Competitive Learning
The approach extends relational Soft Competitive Learning (aka Relational Neural Gas) by a supervised learning scheme which is more reliable than former approaches. The method can
be used to get a (semi-) supervised clustering of (large) proximity matrices.