We propose the Time Frequency Gradient Method (TFGM)
which forms a framework for optimization of models
that are constrained in the time domain while having
efficient representations in the frequency domain.
Since the constraints in the time domain in general
are not transparent in a frequency representation we
demonstrate how the class of objective functions that
are separable in either time or frequency instances allow
the gradient in the time or frequency domain to be
converted to the opposing domain. We further demonstrate
the usefulness of this framework for three different
models; Shifted Non-negative Matrix Factorization,
Convolutive Sparse Coding as well as Smooth
and Sparse Matrix Factorization. Matlab implementation
of the proposed algorithms are available for download
at www.erpwavelab.org.
Publication status | Published - 2009 |
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