ICPR2020 “Wasserstein k-means with sparse simplex projection”
2021年1月10日から15日にミラノ（イタリア）で開催されるパターン認識分野会議 25th International Conference on Pattern Recognition (ICPR) 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）（福永 拓海）の研究論文が採択されました．
- Authors: T. Fukunaga (B4, 4th-year undergraduate student) and HK
- Title: Wasserstein k-means with sparse simplex projection
- Abstract: This paper presents a proposal of a faster Wasserstein k-means algorithm for histogram data by reducing Wasserstein distance computations and exploiting sparse simplex projection. We shrink data samples, centroids, and the ground cost matrix, which leads to considerable reduction of the computations used to solve optimal transport problems without loss of clustering quality. Furthermore, we dynamically reduced the computational complexity by removing lower-valued data samples and harnessing sparse simplex projection while keeping the degradation of clustering quality lower. We designate this proposed algorithm as sparse simplex projection based Wasserstein k-means, or SSPW k-means. Numerical evaluations conducted with comparison to results obtained using Wasserstein k-means algorithm demonstrate the effectiveness of the proposed SSPW k-means for real-world datasets.
- Paper: Publisher site, arXiv preprint arXiv:2011.12542.
Conference information : ICPR
- ICPR is the world largest conference on pattern recognition fields and is held every two years since 1972 under the auspices of the International Association for Pattern Recognition (IAPR). It covers both theoretical issues and applications of the discipline. We solicit original research for publication in the main conference.
- Google Scholar Metric H5-index：38 (checked on Oct. 12, 2020)