Lifelong Spectral Clustering
文献类型：会议论文
| 作者 | Sun G(孙干)1,4 ; Cong Y(丛杨)2; Wang QQ(王倩倩)1; Li, Jun3; Fu Y(付昀)1
|
| 出版日期 | 2020 |
| 会议日期 | February 7-12, 2020 |
| 会议地点 | New York |
| 页码 | 5867-5874 |
| 英文摘要 | In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: Capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: Embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms. |
| 源文献作者 | Association for the Advancement of Artificial Intelligence |
| 产权排序 | 2 |
| 会议录 | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
 |
| 会议录出版者 | AAAI press |
| 会议录出版地 | Palo Alto, CA |
| 语种 | 英语 |
| ISBN号 | 9781577358350 |
| WOS记录号 | WOS:000667722805115 |
| 源URL | [http://ir.sia.cn/handle/173321/28934]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Sun G(孙干) |
| 作者单位 | 1.Northeastern University, United States 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 3.Mit, United States 4.University of Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Sun G,Cong Y,Wang QQ,et al. Lifelong Spectral Clustering[C]. 见:. New York. February 7-12, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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。