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.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

APA

van der Ree, M., Roerdink, J., Phillips, C., Garraux, G., Salmon, E., & Wiering, M. (2017). Support Vector Components Analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN ESANN.

Author

van der Ree, Michiel ; Roerdink, Johannes ; Phillips, Christophe ; Garraux, Gaetan ; Salmon, Eric ; Wiering, Marco. / Support Vector Components Analysis. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN. ESANN, 2017.

Harvard

van der Ree, M, Roerdink, J, Phillips, C, Garraux, G, Salmon, E & Wiering, M 2017, Support Vector Components Analysis. in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN. ESANN, ESANN 2017 - 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 26/04/2017.

Standard

Support Vector Components Analysis. / van der Ree, Michiel; Roerdink, Johannes; Phillips, Christophe; Garraux, Gaetan; Salmon, Eric; Wiering, Marco.

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN. ESANN, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Vancouver

van der Ree M, Roerdink J, Phillips C, Garraux G, Salmon E, Wiering M. Support Vector Components Analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN. ESANN. 2017


BibTeX

@inproceedings{0046e6e535014280a89550675656680f,
title = "Support Vector Components Analysis",
abstract = "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.",
keywords = "Support vector machines, Dimensionality reduction, Machine Learning",
author = "{van der Ree}, Michiel and Johannes Roerdink and Christophe Phillips and Gaetan Garraux and Eric Salmon and Marco Wiering",
year = "2017",
month = "4",
day = "26",
language = "English",
isbn = "978-287587039-1",
booktitle = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",
publisher = "ESANN",

}

RIS

TY - GEN

T1 - Support Vector Components Analysis

AU - van der Ree, Michiel

AU - Roerdink, Johannes

AU - Phillips, Christophe

AU - Garraux, Gaetan

AU - Salmon, Eric

AU - Wiering, Marco

PY - 2017/4/26

Y1 - 2017/4/26

N2 - 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.

AB - 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.

KW - Support vector machines

KW - Dimensionality reduction

KW - Machine Learning

M3 - Conference contribution

SN - 978-287587039-1

BT - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - ESANN

ER -

ID: 41690937