Generative Local Metric Learning for Nearest Neighbor Classification
- Title
- Generative Local Metric Learning for Nearest Neighbor Classification
- Author
- 노영균
- Keywords
- Metric learning; nearest neighbor classification; f-divergence; generative-discriminative hybridization
- Issue Date
- 2018-01
- Publisher
- IEEE COMPUTER SOC
- Citation
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 40, no. 1, page. 106-118
- Abstract
- We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.
- URI
- https://ieeexplore.ieee.org/document/7847425https://repository.hanyang.ac.kr/handle/20.500.11754/117284
- ISSN
- 0162-8828; 1939-3539
- DOI
- 10.1109/TPAMI.2017.2666151
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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