Application of indefinite learning in the life sciences (open access)

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif: Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. 6: 553000 (2020)

Sparsification of core set models in non-metric supervised learning

We sparsified our original proposal of an indefinite core vector machine. 

Fast and sparse - check it out

Frank-Michael Schleif, Christoph Raab, Peter Tiño: Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. 129: 1-7 (2020)

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

• Frank-Michael Schleif, Peter Tiño:
  Indefinite Core Vector Machine. Pattern Recognition 71: 187-195 (2017)

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.

New Pattern Recognition paper on Indefinite Core Vector Machine

Our new article Indefinite Core Vector Machine is now online at Pattern Recognition (Elsevier)
http://www.sciencedirect.com/science/article/pii/S0031320317302261

Highlights

• 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

Accepted paper @ ICANN 2017

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

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.

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)