Faster Ranking Using Extrapolation Techniques

  • Authors:

  • Muhammad Ali Norozi

  • Affiliations:

  • Norwegian University of Science and Technology, Norway

  • Venue:
  • International Journal of Computer Vision and Image Processing
  • Year:
  • 2011

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Abstract

Extrapolations are techniques in linear algebra that require little additional infrastructure that must be incorporated in the existing query-dependent Link Analysis Ranking LAR algorithms. Extrapolations in LAR settings relies on the prior knowledge of the iterative process that created the existing data points iterates to compute the new improved data point, which periodically leads to the desired solution faster than the original method. In this study, the author presents novel approaches using extrapolation techniques to speed-up the convergence of query-dependent iterative methods, link analysis based ranking methods, where hyperlink structures are used to determine relative importance of a document in the network of inter-connections. The author uses the framework defined in HITS and SALSA and proposes the use of different Extrapolation techniques for faster ranking. The paper improves algorithms like HITS and SALSA using Extrapolation techniques. With the proposed approaches it is possible to accelerate the iterative ranking algorithms in terms of reducing the number of iterations and increasing the rate of convergence.