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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    Titel :

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


    Beteiligte:
    Gürvіt, Ercan (Autor:in) / Baykara, N. A. (Autor:in) / Sivasundaram, Seenith (Herausgeber:in)

    Kongress:

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


    Erschienen in:

    Erscheinungsdatum :

    27.01.2017


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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