Invited session proposal “Low-rank approximation”
نویسندگان
چکیده
Low-rank approximations play an important role in systems theory and signal processing. The problems of model reduction and system identification can be posed and solved as a low-rank approximation problem for structured matrices. On the other hand, signal source separation, nonlinear system identification, and multidimensional signal processing and data analysis can be approached with powerful methods of low-rank tensor factorizations. The proposed invited session is focused on theoretical and algorithmic aspects of matrix and tensor low-rank approximations, with applications in system theory and multidimensional signal processing.
منابع مشابه
METT VII — 7th Workshop on Matrix Equations and Tensor Techniques
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A subarea of low-rank approximation that is not covered in this overview is tensors low-rank approximation [WVB10, LV00]. Tensor methods are used in higher order statistical signal processing problems, such as independent component analysis, and multidimensional signal processing, such as spatiotemporal modeling and video processing, to name a few. Other areas of research on low-rank approximat...
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