Kernel-Based Distance Metric Learning In The Output Space
In this paper we present two related, kernelbased Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of...
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ftunicentralflor:oai:stars.library.ucf.edu:scopus2010-6821 2023-05-15T16:01:29+02:00 Kernel-Based Distance Metric Learning In The Output Space Li, Cong Georgiopoulos, Michael Anagnostopoulos, Georgios C. 2013-12-01T08:00:00Z https://stars.library.ucf.edu/scopus2010/5822 unknown STARS https://stars.library.ucf.edu/scopus2010/5822 Scopus Export 2010-2014 text 2013 ftunicentralflor 2022-08-08T17:42:08Z In this paper we present two related, kernelbased Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernelbased DML approaches. © 2013 IEEE. Text DML University of Central Florida (UCF): STARS (Showcase of Text, Archives, Research & Scholarship) |
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University of Central Florida (UCF): STARS (Showcase of Text, Archives, Research & Scholarship) |
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ftunicentralflor |
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description |
In this paper we present two related, kernelbased Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernelbased DML approaches. © 2013 IEEE. |
format |
Text |
author |
Li, Cong Georgiopoulos, Michael Anagnostopoulos, Georgios C. |
spellingShingle |
Li, Cong Georgiopoulos, Michael Anagnostopoulos, Georgios C. Kernel-Based Distance Metric Learning In The Output Space |
author_facet |
Li, Cong Georgiopoulos, Michael Anagnostopoulos, Georgios C. |
author_sort |
Li, Cong |
title |
Kernel-Based Distance Metric Learning In The Output Space |
title_short |
Kernel-Based Distance Metric Learning In The Output Space |
title_full |
Kernel-Based Distance Metric Learning In The Output Space |
title_fullStr |
Kernel-Based Distance Metric Learning In The Output Space |
title_full_unstemmed |
Kernel-Based Distance Metric Learning In The Output Space |
title_sort |
kernel-based distance metric learning in the output space |
publisher |
STARS |
publishDate |
2013 |
url |
https://stars.library.ucf.edu/scopus2010/5822 |
genre |
DML |
genre_facet |
DML |
op_source |
Scopus Export 2010-2014 |
op_relation |
https://stars.library.ucf.edu/scopus2010/5822 |
_version_ |
1766397320984788992 |