@TECHREPORT\{IMM2006-04116,
author = "M. M{\o}rup and M. N. Schmidt",
title = "Sparse Non-negative Matrix Factor {2-D} Deconvolution",
year = "2006",
number = "",
series = "",
institution = "Technical University of Denmark",
address = "",
type = "",
url = "http://www2.imm.dtu.dk/pubdb/p.php?4116",
abstract = "Se instead {''}Shift Invariant Sparse Coding for Image and Music Data{''}
We introduce the non-negative matrix factor {2-D} deconvolution
(NMF2D) model, which decomposes a matrix into a {2-}dimensional
convolution of two factor matrices. This model is an extension of
the non-negative matrix factor deconvolution (NMFD) recently
introduced by Smaragdis (2004). We derive and prove
the convergence of two algorithms for NMF2D based on minimizing the
squared error and the Kullback-Leibler divergence respectively.
Next, we introduce a sparse non-negative matrix factor {2-D}
deconvolution model that gives easy interpretable decompositions
and devise two algorithms for computing this form of factorization. The
developed algorithms have been used for source separation
and music transcription."
}