17 citations · 19 across the 18 of their papers we have counts for
4 papers · 1 filter
Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation
Hayate Kojima, Hiroshi Higashi, Yuichi Tanaka
In this paper, we propose an interpretable denoising method for graph signals using regularization by denoising (RED). RED is a technique developed for image restoration that uses…
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed gr…
Joint Graph Estimation and Signal Restoration for Robust Federated Learning
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central…
Algorithm Unrolling-based Denoising of Multimodal Graph Signals
Hayate Kojima, Keigo Takanami, Junya Hara +4
We propose a denoising method for multimodal graph signals by an alternating minimization scheme that sequentially solves signal restoration and graph learning problems. Many compl…