Yun Chi's Research: Machine Learning Applied in Databases

Identify main data dimensions that are common in most of networked data. Introduce structures into unstructured data, using machine learning techniques. Fuse multiple features for a learning task such as classification. Update learning results in an incremental fashion as new data arriving constantly. Answer questions such as:

How to introduce structures into networked, time-varying, multimedia data, such as blogs?
How to meaningfully combine heterogeneous information such as links, contents, and demographic data in the learning process?
How to conduct OLAP-type operations on the introduced structure?
How to efficiently handle information retrieval on the above data and structure?
How to incrementally incorporate new data by updating previously learned results?

Related publication:

[1]	Yun Chi, Shenghuo Zhu, Koji Hino, Yihong Gong, and Yi Zhang. iOLAP: A framework for analyzing the Internet, social networks, and other networked data. Multimedia, IEEE Transactions on, 11(3):372-382, April 2009. [ bib \| .pdf ]

[2]	Yun Chi, Shenghuo Zhu, Yihong Gong, and Yi Zhang. Probabilistic polyadic factorization and its application to personalized recommendation. In CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 941-950, New York, NY, USA, 2008. ACM. [ bib \| .pdf ]

[3]	Ka Cheung Sia, Junghoo Cho, Yun Chi, and Belle L. Tseng. Efficient computation of personal aggregate queries on blogs. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 632-640, New York, NY, USA, 2008. ACM. [ bib \| .pdf ]

[4]

Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, and Yihong Gong. Integrating clustering and multi-document summarization to improve document understanding. In CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 1435-1436, New York, NY, USA, 2008. ACM. [ bib | .pdf ]

Last updated: Wed Dec 02 16:32:50 PST 2009