Friday, 5 January 2018

Code for indefinite Core Vector Machine (iCVM) published

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
The C++ code provides additionally a new approach to (re-)sparsify the indefinite model
such that the final indefinite decision function remains sparse and permits easy out of sample extension.

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)


  • 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

Sunday, 28 May 2017

Accepted paper @ ICANN 2017

New paper proposing indefinite Support Vector Regression will be presented at ICANN 2017

Saturday, 31 October 2015

Accepted paper@Simbad 2015

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.

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.


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.