EUSIPCO2020 “Consistency- and inconsistency-aware graph-based multi-view clustering”
2021年1月18日から22日にアムステルダム（オランダ）で開催される信号処理分野・欧州フラッグシップ会議 28th International European Signal Processing Conference (EUSIPCO) 2020 に，2019年度プロジェクト研究Bの学部3年生（投稿時，現4年生）（堀江光彦）の研究論文が採択されました．
- Authors: M.Horie (B4, 4th-year undergraduate student) and H.Kasai
- Title: Consistency-aware and inconsistency-aware graph-based multi-view clustering
- Abstract: 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 clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC.
- Paper: Publisher site, arXiv paper.
- Code: The software is available at Github.
Conference information: EUSIPCO
- EUSIPCO is the the flagship conference of EURASIP and offers a comprehensive technical program addressing all the latest developments in research and technology for signal processing. EUSIPCO 2020 will feature world-class speakers, oral and poster sessions, plenaries, exhibitions, demonstrations, tutorials, and satellite workshops, and is expected to attract many leading researchers and industry figures from all over the world. (an excerpt from the web site of EUSIPCO)
- Google Scholar Metric H5-index: 28 (checked on June 4, 2020)