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

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

Wednesday, 22 April 2015

C++ / Armadillo  multicore code for Probabilistic Classification Machine (PCVM) published
see PCVM code

Wednesday, 1 April 2015

The paper about Incremental Probabilistic Classification Vector
Machine with linear costs was accepted at the International Joint Conference on Neural Networks. In the paper we improve the runtime and memory complexity of the Incremental Probabilistic Classification Vector Machine (EPCVM) to linear costs and derive a formulation for a Nystroem approximation on asymmetric matrices.