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.


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    Title :

    Recovery of missing data via wavelets followed by high-dimensional modeling


    Contributors:

    Conference:

    ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences ; 2016 ; La Rochelle, France


    Published in:

    Publication date :

    2017-01-27


    Size :

    7 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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