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Conference papers
Semi-Supervised Learning for Graph to Signal Mapping: a Graph Signal Wiener Filter Interpretation
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.
Cited literature [4 references]
https://hal.inria.fr/hal-00942695
Contributor : Benjamin Girault <>
Submitted on : Wednesday, May 14, 2014 - 10:40:57 AM
Last modification on : Friday, November 6, 2020 - 3:43:55 AM
Long-term archiving on: : Thursday, August 14, 2014 - 11:25:25 AM
Contributor : Benjamin Girault <>
Submitted on : Wednesday, May 14, 2014 - 10:40:57 AM
Last modification on : Friday, November 6, 2020 - 3:43:55 AM
Long-term archiving on: : Thursday, August 14, 2014 - 11:25:25 AM