Dózsa, Tamás and Deuschle, Federico and Cornelis, Bram and Kovács, Péter (2024) Variable projection support vector machines and some applications using adaptive Hermite expansions. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 34 (1). ISSN 0129-0657
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Abstract
In this paper, we develop the so-called variable projection support vector machine (VP-SVM) algorithm that is a generalization of the classical SVM. In fact, the VP block serves as an automatic feature extractor to the SVM, which are trained simultaneously. We consider the primal form of the arising optimization task and investigate the use of nonlinear kernels. We show that by choosing the so-called adaptive Hermite function system as the basis of the orthogonal projections in our classification scheme, several real-world signal processing problems can be successfully solved. In particular, we test the effectiveness of our method in two case studies corresponding to anomaly detection. First, we consider the detection of abnormal peaks in accelerometer data caused by sensor malfunction. Then, we show that the proposed classification algorithm can be used to detect abnormalities in ECG data. Our experiments show that the proposed method produces comparable results to the state-of-the-art while retaining desired properties of SVM classification such as light weight architecture and interpretability. We implement the proposed method on a microcontroller and demonstrate its ability to be used for real-time applications. To further minimize computational cost, discrete orthogonal adaptive Hermite functions are introduced for the first time.
Item Type: | Article |
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Additional Information: | Department of Numerical Analysis, HUN-REN Institute for Computer Science and Control, Eötvös Loránd University, Budapest, H-1111, Hungary Siemens Digital Industries Software, 68 Interleuvenlaan Ku Leuven, Department of Mechanical Engineering, Leuven, B-3001, Belgium Department of Numerical Analysis, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary Export Date: 5 February 2024 Correspondence Address: Kovács, P.; Department of Numerical Analysis, Pázmány Péter sétány 1/C, Hungary; email: kovika@inf.elte.hu |
Uncontrolled Keywords: | Support vector machines; Hermite functions; variable projection; ECG classification; anomaly detection |
Subjects: | Q Science / természettudomány > QA Mathematics / matematika Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány |
SWORD Depositor: | MTMT SWORD |
Depositing User: | MTMT SWORD |
Date Deposited: | 23 Sep 2024 12:39 |
Last Modified: | 23 Sep 2024 12:39 |
URI: | https://real.mtak.hu/id/eprint/205570 |
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