  Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$
approximation of a given input matrix. Selecting the appropriate rank $k$
defines a critical model order choice in most applications of SVD. To obtain a
principled cut-off criterion for the spectrum, we convert the underlying
optimization problem into a noisy channel coding problem. The optimal
approximation capacity of this channel controls the appropriate strength of
regularization to suppress noise. In simulation experiments, this information
theoretic method to determine the optimal rank competes with state-of-the art
model selection techniques.
