Recovery of Missing Data via Wavelets Followed by High-Dimensional Modeling

GÜRVİT E., Baykara N. A.

11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences (ICNPAA), La Rochelle, France, 4 - 08 July 2016, vol.1798 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1798
  • Doi Number: 10.1063/1.4972657
  • City: La Rochelle
  • Country: France
  • Istanbul Technical University Affiliated: Yes


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.