23 citations · 36 across the 9 of their papers we have counts for
4 papers · 1 filter
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space
Jianming Huang, Zhongxi Fang, Hiroyuki Kasai
For graph learning tasks, many existing methods utilize a message-passing mechanism where vertex features are updated iteratively by aggregation of neighbor information. This strat…
Wasserstein k-means with sparse simplex projection
Takumi Fukunaga, Hiroyuki Kasai
This paper presents a proposal of a faster Wasserstein -means algorithm for histogram data by reducing Wasserstein distance computations and exploiting sparse simplex projection…
Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering
Mitsuhiko Horie, Hiroyuki Kasai
Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for c…
Graph embedding using multi-layer adjacent point merging model
Jianming Huang, Hiroyuki Kasai
For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomo…