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Conference Papers Year : 2014

Semi-Supervised Learning for Graph to Signal Mapping: a Graph Signal Wiener Filter Interpretation

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Abstract

In this contribution, we investigate a graph to signal mapping with the objective of analysing intricate structural properties of graphs with tools borrowed from signal processing. We successfully use a graph-based semi-supervised learning approach to map nodes of a graph to signal amplitudes such that the resulting time series is smooth and the procedure efficient and scalable. Theoretical analysis of this method reveals that it essentially amounts to a linear graph-shift-invariant filter with the a priori knowledge put into the training set as input. Further analysis shows that we can interpret this filter as a Wiener filter on graphs. We finally build upon this interpretation to improve our results.
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Dates and versions

hal-00942695 , version 1 (06-02-2014)
hal-00942695 , version 2 (14-05-2014)

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Benjamin Girault, Paulo Gonçalves, Eric Fleury, Arashpreet Singh Mor. Semi-Supervised Learning for Graph to Signal Mapping: a Graph Signal Wiener Filter Interpretation. 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.1115-1119, ⟨10.1109/ICASSP.2014.6853770⟩. ⟨hal-00942695v2⟩
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