Software Seminar

High-Dimensional Similarity Search for Large Datasets

Wei Dong

PhD Candidate
Princeton University
Tuesday, May 10, 2011
3:00pm - 4:00pm
1690 Beyster Bldg.

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About the Event

Images and other non-text feature-rich data are predominant in today's exponentially growing digital universe.How to organize such data at large scale for efficient content-based search is an important problem which remains open after decades of research.One major challenge is that the feature data are usually of high dimensionality and are intrinsically hard to search due to the curse of dimensionality.In this talk, I will present an exciting progress we recently made at Princeton, namely an efficient method to construct a data structure called a k-nearest neighbor graph, which can be used to substantially improve online search.I will also briefly talk about our work on compact data representation for similarity search and on large-scale near-duplicate image detection.


Wei Dong obtained a B.S. from Peking University in 2005 and is now completing his Ph.D. at Princeton with Prof. Kai Li.His research focuses on k-nearest neighbor search in high-dimensional spaces.

Additional Information

Contact: J. Patterson

Phone: 53495

Email: jeannecp

Sponsor(s): CSE

Open to: Public