Large scale indefinite kernel fisher discriminant

Frank Michael Schleif*, Andrej Gisbrecht, Peter Tino

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Indefinite similarity measures can be frequently found in bioinformatics by means of alignment scores. Lacking an underlying vector space, the data are given as pairwise similarities only. Indefinite Kernel Fisher Discriminant (iKFD) is a very effective classifier for this type of data but has cubic complexity and does not scale to larger problems. Here we propose an extension of iKFD such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Evaluation at several larger similarity data from various domains shows that the proposed method provides similar generalization capabilities while being substantially faster for large scale data.

Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition
Subtitle of host publicationThird International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings
EditorsAasa Feragen, Marcello Pelillo, Marco Loog
Number of pages11
ISBN (Electronic) 9783319242613
ISBN (Print)9783319242606
Publication statusPublished - 2015
Event3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 - Copenhagen, Denmark
Duration: 12 Oct 201514 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015

ASJC Scopus subject areas

  • General Computer Science
  • Theoretical Computer Science


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