Learning to Classify with Possible Sensor Failures

Abstract

In this paper, we propose an efficient algorithm to train a robust large-margin classifier, when corrupt measurements caused by sensor failure might be present in the training set. By incorporating a non-parametric prior based on the empirical distribution of the training data, we propose a Geometric- Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method to perform classification and anomaly detection in a joint manner. We demonstrate that our proposed method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate using simulated data and real footstep data.

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Document Details

Document Type
Technical Report
Publication Date
May 04, 2014
Accession Number
ADA617287

Entities

People

  • Alfred O. Hero III
  • Nasser M. Nasrabadi
  • Tianpei Xie

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Detectors
  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Data Sets
  • Detection
  • Detectors
  • Gaussian Distributions
  • Gaussian Processes
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Measurement
  • Signal Processing
  • Supervised Machine Learning
  • Warning Systems

Fields of Study

  • Computer science

Readers

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  • Sensor Fusion and Tracking Systems.