Multi-metric learning for multi-sensor fusion based classification

Authors:
Yanning Zhang;Haichao Zhang;Nasser M. Nasrabadi;Thomas S. Huang
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, China and Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA;U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD, USA;Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
Venue:
Information Fusion
Year:
2013

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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 joint 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 a joint classification. Furthermore, we also exploit multi-metric learning in a kernel induced feature space to capture the non-linearity in the original feature space via kernel mapping.