Accession Number:

ADA617287

Title:

Learning to Classify with Possible Sensor Failures

Descriptive Note:

Conference paper

Corporate Author:

MICHIGAN UNIV ANN ARBOR

Report Date:

2014-05-04

Pagination or Media Count:

7.0

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.

Subject Categories:

  • Electrical and Electronic Equipment
  • Mechanics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE