Publication

Support Vector Components Analysis

van der Ree, M., Roerdink, J., Phillips, C., Garraux, G., Salmon, E. & Wiering, M., 26-Apr-2017, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN. ESANN, 6 p.

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In this paper we propose a novel method for learning a distance metric in the process of training Support Vector Machines (SVMs) with the radial basis function kernel. A transformation matrix is adapted in such a way that the SVM dual objective of a classification problem is optimized. By using a wide transformation matrix the method can effectively be used as a means of supervised dimensionality reduction. We compare our method with other algorithms on a toy dataset and on PET-scans of patients with various Parkinsonisms, finding that our method either outperforms or performs on par with the other algorithms.
Original languageEnglish
Title of host publicationEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Subtitle of host publicationESANN
PublisherESANN
Number of pages6
ISBN (Print)978-287587039-1
Publication statusPublished - 26-Apr-2017
EventESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 26-Apr-201728-Nov-2017

Conference

ConferenceESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
CountryBelgium
CityBruges
Period26/04/201728/11/2017

Event

ESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

26/04/201728/11/2017

Bruges, Belgium

Event: Conference

    Keywords

  • Support vector machines, Dimensionality reduction, Machine Learning

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