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Robust Multidimensional Matched Subspace Classifiers Based on Weighted Least-Squares

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3 Author(s)

We propose and design two classes of robust subspace classifiers for classification of multidimensional signals. Our classifiers are based on robust M-estimators and the least-median-of-squares principle, and we show that they may be unified as iterated reweighted oblique subspace classifiers. The performance of the proposed classifiers are demonstrated by two examples: noncoherent detection of space-time frequency-shift keying signals, and shape classification of partially occluded two-dimensional (2-D)_ objects. In both cases, the proposed robust subspace classifiers outperform the conventional subspace classifiers

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Signal Processing, IEEE Transactions on  (Volume:55 ,  Issue: 3 )