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A variety of problems in remote sensing require that a covariance matrix be accurately estimated, often from a limited number of data samples. We investigate the utility of several variants of a recently introduced covariance estimator-the sparse matrix transform (SMT), a shrinkage-enhanced SMT, and a graph-constrained SMT-in the context of several of these problems. In addition to two more generic...
This paper discusses the use of the Sparse Matrix Transform (SMT) to model the covariance structure of high-dimensional data in the likelihood ratio test used for hypothesis testing. The SMT has been shown to produce more accurate estimates of covariance matrices when the number of training samples n is much less than the number of dimensions p of the data. Several experiments with face recognition...
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