Abstract
Dimensionality reduction is an essential step in various machine learning tasks. Applying classification algorithms to the reduced space is often more efficient and accurate. We focus on kernel based dimensionality reduction techniques, and propose to set the bandwidth such that a coherent mapping is extracted. The proposed framework is simulated on artificial and real dataset, results show a high correlation between optimal classification rates and the proposed bandwidth.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509021529 |
DOIs | |
State | Published - 4 Jan 2017 |
Externally published | Yes |
Event | 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel Duration: 16 Nov 2016 → 18 Nov 2016 |
Publication series
Name | 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 |
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Conference
Conference | 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 |
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Country/Territory | Israel |
City | Eilat |
Period | 16/11/16 → 18/11/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.