3 papers
cs.IT2026
Lossy compression of weighted graph adjacency matrices by transform coding
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
In this paper, we propose a compression framework for weighted graphs in which the graph topology is transmitted losslessly and edge weights are compressed lossily. A challenge in…
eess.SP2024
Lossy Compression of Adjacency Matrices by Graph Filter Banks
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations o…
cs.LG2024
Optimizing in NN Graphs with Graph Learning Perspective
Asuka Tamaru, Junya Hara, Hiroshi Higashi +2
In this paper, we propose a method, based on graph signal processing, to optimize the choice of in -nearest neighbor graphs (NNGs). NN is one of the most popular appro…