DOI: 10.1109/acssc.2004.1399592
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Abstract: We consider subspace learning from measurements corrupted by log-concave random noise. The class includes, but is not limited to, Generalized Gaussian (GG) noise with shape parameter greater than or equal to unity, log-concave Spherically Invariant Random Processes (SIRP's), and their generalizations to Norm Invariant Random Processes (NIRP's). The noise properties need not be constant in the independent time and/or space variable. Necessary conditions are derived and they are computationally simpler when fac…

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