PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Neighbourhood components analysis
Jacob Goldberger, Sam Roweis, Geoff Hinton and Ruslan Salakhutdinov
In: NIPS 2004, 13-18 Dec 2004, Vancover, Canada.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1570
Deposited By:Jacob Goldberger
Deposited On:28 November 2005