Neighbourhood Components Analysis

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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov
• Keywords: KNN Classification Algorithm, Leave-one-out Knn Score, Mahalanobis Distance Measure, NIPS 2004, NIPS 2007