Abstract
We propose a novel graph filtering method for semi-supervised classification that adopts multiple graph shift matrices to obtain more flexibility in dealing with misleading features. The resulting optimization problem is solved with a computationally efficient alternating minimization approach. In simulation experiments, we implement both conventional and our proposed graph filters as semi-supervised classifiers on real and synthetic datasets to demonstrate advantages of our algorithms in terms of classification performance.
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