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Publikováno v:
ICASSP
In this paper, we consider the reconstruction of a high-dimensional seismic volume with randomly missing traces. Seismic data in the frequency-space domain are represented via a high-order tensor. Applying the parallel matrix factorization model to t
Publikováno v:
ICASSP
The nonnegative matrix factorization (NMF) has been a popular model for a wide range of signal processing and machine learning problems. It is usually formulated as a nonconvex cost minimization problem. This work settles the convergence issue of a p
Publikováno v:
ICASSP
A video taken under the influence of atmospheric turbulence suffers from serious distortion caused by the variation of optical refractive index. In order to reduce geometric distortion and time-space-varying blur, and recover both coarse structure an
Publikováno v:
ICASSP
Non-negative matrix factorization (NMF) has found use in fields such as remote sensing and computer vision where the signals of interest are usually non-negative. Data dimensions in these applications can be huge and traditional algorithms break down
Publikováno v:
ICASSP
Low-rank matrix factorization serves as a key technique in learning latent factor models for many applications in machine learning. However, in many applications, observed data often exhibits different levels of noise. To address this issue, we propo
Autor:
Christian Jutten, Dana Lakat
Publikováno v:
ICASSP
In this paper, we present an alternative proof for characterizing the (non-) identifiability conditions of independent vector analysis (IVA). IVA extends blind source separation to several mixtures by taking into account statistical dependencies betw