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Beyond Mahalanobis Metric: Cayley-Klein Metric Learning
Bi, Yanhong; Fan, Bin; Wu, Fuchao
2015
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2015
Conference PlaceBoston, Massachusetts, USA
Abstract
Cayley-Klein metric is a kind of non-Euclidean metric suitable for projective space. In this paper, we introduce it into the computer vision community as a powerful metric and an alternative to the widely studied Mahalanobis metric. We show that besides its good characteristic in non-Euclidean space, it is a generalization of Mahalanobis metric in some specific cases. Furthermore, as many Mahalanobis metric learning, we give two kinds of Cayley-Klein metric learning methods: MMC Cayley-Klein metric learning and LMNN Cayley-Klein metric learning. Experiments have shown the superiority of Cayley-Klein metric over Mahalanobis ones and the effectiveness of our Cayley-Klein metric learning methods.
KeywordMetric Learning Cayley-klein Metric
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Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19822
Collection模式识别国家重点实验室_机器人视觉
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Bi, Yanhong,Fan, Bin,Wu, Fuchao. Beyond Mahalanobis Metric: Cayley-Klein Metric Learning[C],2015.
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