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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2023-03-15 11:20 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Optimizing SOINN Space for High-Dimensional Data Robustness Yu Takahagi, Yusuke Tsuchida, Yukari Yamauchi (Nihon Univ.)  NC2022-112 |
Yamazaki et al. proposed a learning method called Self-Organizing Incremental Neural Network (SOINN). This method is an ... [more] | NC2022-112 pp.113-118 |
| DE, IPSJ-DBS | 2021-12-27 15:00 |
Online | (Primary: Online, Secondary: On-site) |
GPU-accelerated reverse k-nearest neighbor search for high-dimensional data Kyohei Tsuihiji (Univ. of Tsukuba), Toshiyuki Amagasa (CCS) DE2021-18 |
(To be available after the conference date) [more] | DE2021-18 pp.19-24 |
| QIT (2nd) |
2021-05-25 14:00 |
Online | Online | Quantum-inspired principal component analysis for high-dimensional data Kei Majima (QST), Naoko Koide-Majima (NICT), Hiroyuki Takuwa, Makoto Higuchi, Tetsuya Suhara, Noriaki Yahata (QST) |
Principal component analysis (PCA) is a widely used statistical tool for extracting low-dimensional structures underlyin... [more] | |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2016-07-06 14:55 |
Okinawa | Okinawa Institute of Science and Technology | Classification analysis of high-dimensional data based on L0-norm optimization. Noriki Ito, Masashi Sato (UEC Tokyo), Yoshiyuki Kabashima (Tokyo Tech), Yoichi Miyawaki (UEC Tokyo) NC2016-14 |
Advances in sensing devices allow us to measure high-dimensional data easily, but the sample size is often limited becau... [more] | NC2016-14 pp.223-228 |
| IBISML | 2012-03-13 10:00 |
Tokyo | The Institute of Statistical Mathematics | On d-consistency for high-dimensional discrimination analysis Takanori Ayano (Osaka Univ.) IBISML2011-99 |
Recently, in many fields such as microarray analysis, we need to analyze high-dimensional data with small sample sizes. ... [more] | IBISML2011-99 pp.85-88 |
| IBISML | 2010-06-15 10:25 |
Tokyo | Takeda Hall, Univ. Tokyo | [Invited Talk]
Statistical testing with large multiplicity Shigeyuki Oba (Kyoto Univ./JST) IBISML2010-15 |
Statistical hypothesis testing is a basic tool in broad areas of scientific studies and guarantees that an assertion... [more] |
IBISML2010-15 pp.95-102 |
| DE | 2007-07-03 15:25 |
Miyagi | Akiu hot springs (Sendai) | Parallel Frequent Pattern Mining Method from Super High-Dimensional Data by Vertical Partitioning Kouichirou Mori, Ryohei Orihara (Toshiba Corp.) DE2007-91 |
In general, traditional parallel frequent pattern mining methods were applied to data that contains a large number of re... [more] | DE2007-91 pp.417-422 |
| PRMU, NLC | 2005-02-25 15:30 |
Tokyo | [Special Talk]
unkown Shin'ichi Satoh (NII) |
In retrieval or mining of high-dimensional multimedia database, it is much costly in terms of computation to discard maj... [more] | NLC2004-129 PRMU2004-211 pp.79-84 |
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