23 citations · 25 across the 7 of their papers we have counts for
8 papers
Unrolled Time-Varying Graph Signal Restoration under Spatiotemporal Smoothness Priors
Hayate Kojima, Hikari Noguchi, Koki Yamada +1
In this paper, we propose restoration methods for time-varying graph signals using deep algorithm unrolling (DAU). Time-varying graph signals, such as signals obtained from sensor…
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness
Kenta Yanagiya, Koki Yamada, Yasuo Katsuhara +2
Finding important edges in a graph is a crucial problem for various research fields, such as network epidemics, signal processing, machine learning, and sensor networks. In this pa…
Versatile Time-Frequency Representations Realized by Convex Penalty on Magnitude Spectrogram
Keidai Arai, Koki Yamada, Kohei Yatabe
Sparse time-frequency (T-F) representations have been an important research topic for more than several decades. Among them, optimization-based methods (in particular, extensions o…
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
Katsuki Fukumoto, Koki Yamada, Yuichi Tanaka +1
We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tas…
Graph Filter Transfer via Probability Density Ratio Weighting
Koki Yamada
The problem of recovering graph signals is one of the main topics in graph signal processing. A representative approach to this problem is the graph Wiener filter, which utilizes t…
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka +2
Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Ofte…
Graph Blind Deconvolution with Sparseness Constraint
Kazuma Iwata, Koki Yamada, Yuichi Tanaka
We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal. Graph blind deconvolution is an algorithm for estimatin…
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
Haruki Yokota, Koki Yamada, Yuichi Tanaka +1
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method…