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.

Wednesday, 29 April 2015

PCVM library code announced at MLOSS

The pcvm library code has been announced at mloss.org

PCVM C++/armadillo library 0.2

New version of the PCVM C++ code is now available including
the usage of Nystroem approximated kernel (see Readme).
The code now also contains concepts published at ESANN 2015 see

It may also be interesting if you would like to have a C++/Armadillo implementation
of a Nystroem approximated singular value decomposition (SVD), eigenvalue decomposition (EVD)
or pseudo inverse calculation (PINV). Further it contains code for the normCDF and normPDF.

The code is available at PCVM code