6 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Heterogeneous co-occurrence embedding for visual information exploration
Takuro Ishida, Tetsuo Furukawa
This paper proposes an embedding method for co-occurrence data aimed at visual information exploration. We consider cases where co-occurrence probabilities are measured between pai…
cs.LG2021★ 1 cited
Multi-task manifold learning for small sample size datasets
Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa
In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task…
cs.HC2021★ 6 cited
Visual analytics of set data for knowledge discovery and member selection support
Ryuji Watanabe, Hideaki Ishibashi, Tetsuo Furukawa
Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered ma…