Non-linear Neighborhood Component Analysis Based on Constructive Neural Networks

Chen Qin, Shiji Song, Gao Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we propose a novel non-linear supervised metric learning algorithm. The algorithm combines the neighborhood component analysis method with constructive neural networks which gradually increase the network size during the training process. The network aims to maximize a stochastic variant of the leave-one-out K-nearest neighbor (KNN) score on the training set. In this way, the proposed algorithm learns a nonlinear metric for KNN classification, overcoming the limitations of traditional metric learning algorithms which are only capable of learning linear transformations. Therefore, the proposed method is more flexible and powerful in transforming data than its linear counterpart. Moreover, it can also learn a low-dimensional non-linear mapping for visualization and fast classification. We validate our method on several benchmark datasets both for metric learning and dimensionality reduction, and the results demonstrate the competitiveness of the proposed approach.
Original languageEnglish
Title of host publicationIEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)
PublisherIEEE Xplore
Pages1997-2002
ISBN (Print)978-1-4799-3840-7
DOIs
Publication statusPublished - 4 Dec 2014

Publication series

NameIEEE
ISSN (Print)1062-922X

Fingerprint

Dive into the research topics of 'Non-linear Neighborhood Component Analysis Based on Constructive Neural Networks'. Together they form a unique fingerprint.

Cite this