Heterogeneous multi-metric learning for multi-sensor fusion

Haichao Zhang, Nasser M. Nasrabadi, Thomas S. Huang, Yanning Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for classification. The learned metrics have the potential to perform better than the conventional Euclidean metric for classification. Moreover, in the case of heterogenous sensors, the learned multiple metrics can be quite different, which are adapted to each type of sensor. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to fuse the multi-sensor data for joint classification.

Original languageEnglish (US)
Title of host publicationFusion 2011 - 14th International Conference on Information Fusion
StatePublished - Sep 13 2011
Event14th International Conference on Information Fusion, Fusion 2011 - Chicago, IL, United States
Duration: Jul 5 2011Jul 8 2011

Publication series

NameFusion 2011 - 14th International Conference on Information Fusion

Other

Other14th International Conference on Information Fusion, Fusion 2011
Country/TerritoryUnited States
CityChicago, IL
Period7/5/117/8/11

Keywords

  • Metric learning
  • Multi-sensor fusion

ASJC Scopus subject areas

  • Information Systems

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