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, Fransa, 4 - 08 Temmuz 2016, cilt.1798 identifier identifier

  • Cilt numarası: 1798
  • Doi Numarası: 10.1063/1.4972657
  • Basıldığı Şehir: La Rochelle
  • Basıldığı Ülke: Fransa

Özet

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.