Multiresolution Approach in Computing NTF

  • Arto Kaarna
  • Alexey Andriyashin
  • Shigeki Nakauchi
  • Jussi Parkkinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


The computation of non-negative tensor factorization may become very time-consuming when large datasets are used. This study shows how to accelerate NTF using multiresolution approach. The large dataset is preprocessed with an integer wavelet transform and NTF results from the low resolution dataset are utilized in the higher resolution dataset. The experiments show that the multiresolution based speed-up for NTF computation varies in general from 2 to 10 depending on the dataset size and on the number of required basis functions.


Facial Image Nonnegative Matrix Factorization Tensor Factorization Integer Wavelet Multiresolution Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Arto Kaarna
    • 1
    • 2
  • Alexey Andriyashin
    • 3
  • Shigeki Nakauchi
    • 2
  • Jussi Parkkinen
    • 3
  1. 1.Lappeenranta University of Technology, Department of Information Technology, P.O. Box 20, FIN-53851 LappeenrantaFinland
  2. 2.Toyohashi University of Technology, Department of Information and Computer Sciences, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi, 441-8580Japan
  3. 3.University of Joensuu, Laboratory of Computer Science, P.O. Box 111, FI-80101 JoensuuFinland

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