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)

Tuesday, 6 June 2017

New Pattern Recognition paper on Indefinite Core Vector Machine

Our new article Indefinite Core Vector Machine is now online at Pattern Recognition (Elsevier)
http://www.sciencedirect.com/science/article/pii/S0031320317302261

Highlights

Indefinite Core Vector Machine (iCVM) is proposed
approximation concepts are provided leading to linear runtime complexity under moderate assumptions
sparsification of iCVM is proposed showing that in many cases also a low memory complexity can be obtained with an acceptable loss in accuracy
the algorithm is compared to a number of related methods and multiple datasets showing competitive performance but with much lower computational and memory complexity