BigData: Probabilistic Methods for Efficient Search and Statistical Learning in
Extremely High-Dimensional Data
Monday, October 8, 2012 - 4:00pm - 4:50pm
KEC 1003
Ping Li
Assistant Professor
Department of Statistical Science
Cornell University
Speaker Biography:
Ping Li is an Assistant Professor in the Department of Statistical Science at Cornell University. His research interests include BigData, randomized algorithms, boosting and trees, information retrieval, etc. Ping Li won a prize in the Yahoo! 2010 Learning to Rank Grand Challenge. He is also a recipient of the ONR (Office of Naval Research) Young Investigator Award in 2009.
Abstract:
This talk will present a series of work on probabilistic hashing methods which
typically transform a challenging (or infeasible) massive data computational
problem into a probability and statistical estimation problem. For example,
fitting a logistic regression (or SVM) model on a dataset with billion
observations and billion (or billion square) variables would be difficult.
Searching for similar documents (or images) in a repository of billion web
pages (or images) is another challenging example. In certain important
applications in the search industry, a web page is often represented as a
binary (0/1) vector in billion square (2 to power 64) dimensions. For those
data, both data reduction (i.e., reducing number of nonzero entries) and
dimensionality reduction are crucial for achieving efficient search and
statistical learning.
This talk will present two closely related probabilistic methods: (1) b-bit
minwise hashing and (2) one permutation hashing, which simultaneously perform
effective data reduction and dimensionality reduction on massive,
high-dimensional, binary data. For example, training an SVM for classification
on a text dataset of size 24GB took only 3 seconds after reducing the dataset
to merely 70MB using our probabilistic methods. Experiments on close to 1TB
data will also be presented. Several challenging probability problems still
remain open.
Key references:
[1] P. Li, A. Owen, C-H Zhang, On Permutation Hashing, NIPS 2012;
[2] P. Li, C. Konig, Theory and Applications of b-Bit Minwise Hashing, Research
Highlights in Communications of the ACM 2011.
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