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Abstract
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 language | English |
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Title of host publication | Similarity-Based Pattern Recognition |
Subtitle of host publication | Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings |
Editors | Aasa Feragen, Marcello Pelillo, Marco Loog |
Publisher | Springer |
Pages | 160-170 |
Number of pages | 11 |
Volume | 9370 |
ISBN (Electronic) | 9783319242613 |
ISBN (Print) | 9783319242606 |
DOIs | |
Publication status | Published - 2015 |
Event | 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 - Copenhagen, Denmark Duration: 12 Oct 2015 → 14 Oct 2015 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9370 |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 |
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Country/Territory | Denmark |
City | Copenhagen |
Period | 12/10/15 → 14/10/15 |
ASJC Scopus subject areas
- General Computer Science
- Theoretical Computer Science
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Personalised Medicine through Learning in the Model Space
Engineering & Physical Science Research Council
1/10/13 → 31/03/17
Project: Research Councils