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【学术报告】Matrix optimization models and algorithms for Data Clustering
2018-07-17 10:03  
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时间:2018年7月17日14:30


地点:基础教学部C08-201




报告题目:Matrix optimization models and algorithms for Data Clustering

报告人:陈飞宇

报告人简介

陈飞宇,助理研究员,博士。

重庆大学智能服务与软件工程中心研究骨干,重庆市农业大数据产业技术协同创新中心研究骨干。

主要研究方向:机器学习、数值优化。

Email: fchen@cqu.edu.cn

报告摘要:

Abstract: Clustering is to classify data into groups according to a predefined distance or similarity measure. It has wide applications in data mining, pattern recognition, image processing and other machine learning areas. It is well known that lots of clustering models, like K-means and K-indicators, can be written as non-convex matrix optimization problems.

In this work, we attempt to employ the classical optimization algorithms to solve the unsupervised clustering task. Numerical examples on several benchmark datasets are conducted to evaluate the efficiency and accuracy of our approach.


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