Linear Dimensionality Reduction for Margin-Based Classification: High-Dimensional Data and Sensor Networks

@article{Varshney2011LinearDR,
  title={Linear Dimensionality Reduction for Margin-Based Classification: High-Dimensional Data and Sensor Networks},
  author={Kush R. Varshney and Alan S. Willsky},
  journal={IEEE Transactions on Signal Processing},
  year={2011},
  volume={59},
  pages={2496-2512}
}
Low-dimensional statistics of measurements play an important role in detection problems, including those encountered in sensor networks. In this work, we focus on learning low-dimensional linear statistics of high-dimensional measurement data along with decision rules defined in the low-dimensional space in the case when the probability density of the measurements and class labels is not given, but a training set of samples from this distribution is given. We pose a joint optimization problem… CONTINUE READING
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