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An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction
Recently, a recommendation algorithm based on the graph Laplacian regularization was proposed, which treats the prediction problem of the recommendation system as a reconstruction issue of small samples of the graph signal under the same graph model.
Spline-Like Wavelet Filterbanks with Perfect Reconstruction on Arbitrary Graphs
In this work, we propose a class of spline-like wavelet filterbanks for graph signals.
Perfect Reconstruction Two-Channel Filter Banks on Arbitrary Graphs Based on an Optimization Model
In this paper, we propose the construction of critically sampled perfect reconstruction two-channel filterbanks on arbitrary undirected graphs. Inspired by the design of graphQMF proposed in the literature, we propose a general ``spectral folding property'' similar to that of bipartite graphs and provide sufficient conditions for constructing perfect reconstruction filterbanks based on a general graph Fourier basis, which is not the eigenvectors of the Laplacian matrix.
Atomic Filter: a Weak Form of Shift Operator for Graph Signals
In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals.
