Subspace learning in non-Gaussian log-concave noise

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 (SIRPs), and their generalizations to norm invariant random processes (NIRPs). 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 factorability properties are applicable, as is the case with GG´s, SIRP´s, and NIRPs when the signal space is one-dimensional.