In this article missing multi-dimensional data imputation is taken into consideration for unevenly spaced data. The only prerequisite information is intended to be the knowledge that would allow us to guess a matrix called a frame. As an example in image processing an inverse discrete cosine transform matrix would be a suitable frame. The main purpose here is to guess such a sparse frame that can represent complete data vector f. By a sparse representation we mean the majority of components being close to zero. In the present article the data imputation using the expected sparse representation is intended to be done in a wavelet or lifting scheme basis. Finally, the generalization to multivariate case will be discussed.
Recovery of missing data via wavelets followed by high-dimensional modeling
ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences ; 2016 ; La Rochelle, France
AIP Conference Proceedings ; 1798 , 1
2017-01-27
7 pages
Conference paper
Electronic Resource
English
Biorthogonal Wavelets for Data Compression
NTRS | 1994
|Data compression with spherical wavelets and wavelets for the image-based relighting
British Library Online Contents | 2004
|Modeling Propagation in Optical Fibers Using Wavelets
British Library Online Contents | 1994
|High-order symmetrical hyperbolic wavelets
Online Contents | 2007
|Data Compression with Hybrid Supercompact Wavelets
AIAA | 2003
|