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Interpolation of sparse graph signals by sequential adaptive thresholds

Boloursaz Mashhadi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/SAMPTA.2017.8024339
  3. Abstract:
  4. This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been low-pass/band-limitedness of the underlying graph signal. However, inspired by the findings on sparse signal reconstruction, we consider the graph signal to be rather sparse/compressible in the Graph Fourier Transform (GFT) domain and propose the Iterative Method with Adaptive Thresholding for Graph Interpolation (IMATGI) algorithm for sparsity promoting interpolation of the underlying graph signal. We analytically prove convergence of the proposed algorithm. We also demonstrate efficient performance of the proposed IMATGI algorithm in reconstructing randomly generated sparse graph signals. Finally, we consider the widely desirable application of recommendation systems and show by simulations that IMATGI outperforms state-of-the-art algorithms on the benchmark datasets in this application. © 2017 IEEE
  5. Keywords:
  6. Graph signal interpolation ; Recommendation systems ; The iterative method with adaptive thresholding for graph interpolation (IMATGI) ; Benchmarking ; Interpolation ; Iterative methods ; Recommender systems ; Signal reconstruction ; Adaptive thresholding ; Adaptive thresholds ; Benchmark datasets ; Graph Fourier transforms ; Signal interpolation ; Sparse signal reconstruction ; State-of-the-art algorithms ; Underlying graphs ; Graph theory
  7. Source: 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 266-270 ; 9781538615652 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/8024339