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Found 6 papers, 0 papers with code
Versatile Time-Frequency Representations Realized by Convex Penalty on Magnitude Spectrogram
Sparse time-frequency (T-F) representations have been an important research topic for more than several decades.
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels.
Graph Filter Transfer via Probability Density Ratio Weighting
A representative approach to this problem is the graph Wiener filter, which utilizes the statistical information of the target signal computed from historical data to construct an effective estimator.
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually.
Graph Blind Deconvolution with Sparseness Constraint
We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal.
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements.
