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Discrimination on the Grassmann Manifold: Fundamental Limits of Subspace Classifiers
We present fundamental limits on the reliable classification of linear and
affine subspaces from noisy, linear features. Drawing an analogy between
discrimination among subspaces and communication over vector wireless channels,
we propose two Shannon-inspired measures to characterize asymptotic classifier
performance. First, we define the classification capacity, which characterizes
necessary and sufficient conditions for the misclassification probability to
vanish as the signal dimension, the number of features, and the number of
subspaces to be discerned all approach infinity. Second, we define the
diversity-discrimination tradeoff which, by analogy with the
diversity-multiplexing tradeoff of fading vector channels, characterizes
relationships between the number of discernible subspaces and the
misclassification probability as the noise power approaches zero. We derive
upper and lower bounds on these measures which are tight in many regimes.
Numerical results, including a face recognition application, validate the
results in practice.Comment: 19 pages, 4 figures. Revised submission to IEEE Transactions on
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