maximum volume clustering
文献类型：会议论文
| 作者 | Gang Niu ; Bo Dai ; Lin Shang ; Masashi Sugiyama |
| 出版日期 | 2011 |
| 会议名称 | 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011 |
| 会议日期 | April 11, 2011 - April 13, 2011 |
| 会议地点 | Fort Lauderdale, FL, United states |
| 关键词 | Artificial intelligence Clustering algorithms Equivalence classes Sampling |
| 页码 | 561-569 |
| 中文摘要 | The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a clustering model based on the large volume principle called maximum volume clustering (MVC), and propose two algorithms to solve it approximately: a soft-label and a hard-labelMVC algorithms based on sequential quadratic programming and semi-definite programming, respectively. Our MVC model includes spectral clustering and maximum margin clustering as special cases, and is substantially more general. We also establish the finite sample stability and an error bound for soft-label MVC method. Experiments show that the proposed MVC approach compares favorably with state-of-the-art clustering algorithms. Copyright 2011 by the authors. |
| 英文摘要 | The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a clustering model based on the large volume principle called maximum volume clustering (MVC), and propose two algorithms to solve it approximately: a soft-label and a hard-labelMVC algorithms based on sequential quadratic programming and semi-definite programming, respectively. Our MVC model includes spectral clustering and maximum margin clustering as special cases, and is substantially more general. We also establish the finite sample stability and an error bound for soft-label MVC method. Experiments show that the proposed MVC approach compares favorably with state-of-the-art clustering algorithms. Copyright 2011 by the authors. |
| 收录类别 | EI |
| 会议主办者 | Google; Microsoft; PASCAL2; IBM; NEC - Epowered by Innovation |
| 会议录 | Journal of Machine Learning Research
 |
| 语种 | 英语 |
| ISSN号 | 1532-4435 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16311]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Gang Niu,Bo Dai,Lin Shang,et al. maximum volume clustering[C]. 见:14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011. Fort Lauderdale, FL, United states. April 11, 2011 - April 13, 2011. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。