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)
A simplified matlab code and an armadillo/C++ implementation of the indefinite
core vector machine (iCVM) is published at iCVM - Indefinite Core Vector Machine
and MLOSS it implements ideas published in the paper
Got a paper accepted about Large scale Indefinite Kernel Fisher Discriminant
to be presented at the next Simbad Workshop. We present a way to get linear
runtime complexity for the iKFD algorithm given the input matrix is (approximately)
low rank. The original iKFD has cubic runtime complexity. iKFD is a very good
classification algorithm for indefinite / non-metric / non-positive input kernels and
our proposal makes iKFD ready for large scale problems.