Neighbourhood components analysis (2004)
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| Venue: | Advances in Neural Information Processing Systems 17 |
| Citations: | 129 - 5 self |
BibTeX
@INPROCEEDINGS{Goldberger04neighbourhoodcomponents,
author = {Jacob Goldberger and Sam Roweis and Geoff Hinton and Ruslan Salakhutdinov},
title = {Neighbourhood components analysis},
booktitle = {Advances in Neural Information Processing Systems 17},
year = {2004},
pages = {513--520},
publisher = {MIT Press}
}
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Abstract
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction. 1
Citations
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| 17 | Learning distance functions using equivalence relations - Hillel, Hertz, et al. |
| 13 | Similarity metric learning for a variable kernel classifier - Lowe - 1995 |
| 12 | Robust linear dimensionality reduction - Koren, Carmel |
| 2 | Distance learning metric - Xing, Ng, et al. - 2003 |
