Heterogeneous Multi-Metric Learning for Multi-Sensor Fusion

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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2011
Accession Number
ADA565867

Entities

People

  • Haichao Zhang
  • Nasser M. Nasrabadi
  • Thomas Huang
  • Yanning Zhang

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Detectors
  • Acoustic Signals
  • Algorithms
  • Classification
  • Computations
  • Data Fusion
  • Detectors
  • Dimensionality Reduction
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Military Research
  • Pattern Recognition
  • Sensor Fusion
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science
  • Engineering

Readers

  • Neural Network Machine Learning.