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Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 184-185

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From page 184...
... Wohlberg Los Alamos National Laboratory Los Alamos, NM 87545 USA Abstract—We present a technique for combining prior knowledge about transformations that should be ignored with a covariance matrix estimated from training data to make an improved Mahalanobis distance classifier. Modern classification problems often involve objects represented by high-dimensional vectors or images (for example, sampled speech or human faces)
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From page 185...
... a sharp boundary in the background, then requiring ITCH to be small might prevent parameter excursions that would only disrupt the background. To address this objection, we use the eigenvalues of the pooled within class covariance matrix Cw to quantify the importance of the components.
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Key Terms

  • training data
  • covariance matrix
  • image space
  • mahalanobis distance
  • recognition task


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