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)

Friday, 29 May 2015

Review paper on Indefinite proximity learning @ NECO accepted

The paper Indefinite proximity learning - A review by
Frank-Michael Schleif and Peter Tino was accepted for publication
in Neural Computation - the paper is available as open access
(just click on the paper title)

Tuesday, 12 May 2015

New article about conversion of proximity data published

Our article

Metric and non-metric proximity transformations at linear costs

is now online available at Elsevier / Neurocomputing.

Highlights

•: We propose a linear time and memory efficient approach for converting low rank dissimilarity matrices to similarity matrices and vice versa.
•: Our approach is applicable for proximities obtained from non-metric proximity measures (indefinite kernels, non-standard dissimilarity measures).
•: The presented approach also comprises a generalization of Landmark MDS – the presented approach is in general more accurate and flexible than Landmark MDS.
•: We provide an alternative derivation of the Nyström approximation together with a convergence proof, also for indefinite kernels not given in the workshop paper as a core element of the approach.