Title :
Recovering tensor data from incomplete measurement via compressive sampling
Author :
Holloway, Jason R. ; Navasca, Carmeliza
Author_Institution :
Dept. of Electr. Eng., Clarkson Univ., Potsdam, NY, USA
Abstract :
We present a method for recovering tensor data from few measurements. By the process of vectorizing a tensor, the compressed sensing techniques are readily applied. Our formulation leads to three ¿1 minimizations for third order tensors. We demonstrate our algorithm on many random tensors with varying dimensions and sparsity.
Keywords :
signal sampling; compressed sensing techniques; compressive sampling; incomplete measurement; tensor data recovery; tensor vectorization; Compressed sensing; Electric variables measurement; Image sampling; Mathematics; Proposals; Sampling methods; Signal sampling; Sparse matrices; Tensile stress; Text mining;
Conference_Titel :
Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-5825-7
DOI :
10.1109/ACSSC.2009.5469923
