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20180516-13 彭冲:Learning 2D features of 2D data for clustering

2018-5-10 18:51| 发布者: 程一-计算所| 查看: 841| 评论: 0

摘要: 报告嘉宾:彭冲(青岛大学)报告时间:2018年05月16日(星期三)晚上20:00(北京时间)报告题目:Learning 2D features of 2D data for clustering主持人:张健(King Abdullah University of Science and Technolog ...

报告嘉宾:彭冲(青岛大学)

报告时间:2018年05月16日(星期三)晚上20:00(北京时间)

报告题目:Learning 2D features of 2D data for clustering

主持人:张健(King Abdullah University of Science and Technology


报告人简介:

彭冲毕业于南伊利诺伊大学,2017年获计算机科学博士学位,现为青岛大学计算机科学技术学院讲师,青岛大学青年卓越人才。他在AAAI,KDD,CVPR,ICDM,CIKM,ICDE等会议和IEEE TGRS,SPL,ACM TKDD,TIST等期刊发表论文20余篇。


个人主页:

http://cst.qdu.edu.cn/info/1021/2569.htm


相关文献: 

1. Subspace clustering using log-determinant rank approximation, Chong Peng, Zhao Kang, Huiqing Li, Qiang Cheng, KDD 2015.

2. Subspace clustering via variance regularized ridge regression, Chong Peng, Zhao Kang, Qiang Cheng, CVPR 2017.

3. Integrate and conquer: double-sided two-dimensional k-means via integrating of projection and manifold construction, ACM TIST, to appear.


报告摘要:

As fundamental task in unsupervised learning, clustering has been widely studied in data mining and machine learning community. When the inputs are 2-dimensional (2D) data, most existing clustering methods convert such data to vectors as preprocessing, which severely damages spatial information of the data. How to preserve 2D features of the data in the learning process is a key problem. In this talk, I will introduce a novel method for clustering 2D data. First, I will introduce the subspace clustering theory. Based on this, I will then present subspace clustering via variance regularized ridge regression. In addition, I will present a generalized way for clustering 2D data by extending K-means to 2D scenario.


参与方式:


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特别鸣谢本次Webinar主要组织者:

VOOC责任委员:张健(King Abdullah University of Science and Technology

VODB协调理事:郑海永(中国海洋大学



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