Title :
A latent tensor factorization framework for non-negative convolutive models
Author :
Umut Şimşekli;Yusuf Cem Sübakan;Ali Taylan Cemgil
Author_Institution :
Bilgisayar Mü
fDate :
4/1/2011 12:00:00 AM
Abstract :
Convolutive models emerge in various domains such as acoustics, image processing or seismic sciences. In this work, we investigate the convolutive models and the related deconvolution problems in a latent tensor factorization framework. We decrease the computational complexity of the inference scheme by utilizing the Fast Fourier Transform. We also demonstrate how this framework can be used in image deblurring and in more complex models like Non-Negative Matrix Factor Deconvolution (NMFD) model.
Keywords :
"Mathematical model","Computational modeling","Signal processing","Conferences","Tensile stress","Deconvolution","Independent component analysis"
Conference_Titel :
Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on
Print_ISBN :
978-1-4577-0462-8
DOI :
10.1109/SIU.2011.5929762