Probabilistic Hashing Methods for Fitting Massive Logistic Regressions and SVM with Billions of Variables

Ping Li

Department of Statistical Science

Cornell University

Johnson Center 3rd Floor- Meeting Room B

4400 University Drive, Fairfax, VA 22030

Time: 11 am 12 pm

Date: Friday, February 24, 2012


In modern applications, many statistics tasks such as classification using logistic regression or SVM often encounter extremely high-dimensional massive datasets. In the context of search, certain industry applications have used datasets in 264 dimensions, which are larger than the square of billion. This talk will introduce a recent probabilistic hashing technique called b-bit minwise hashing (Research Highlights in Comm. of ACM 2011), which has been used for efficiently computing set similarities in massive data. Most recently (NIPS 2011), we realized that b-bit minwise hashing can be seamlessly integrated with statistical learning algorithms such as logistic regression or SVM to solve extremely large-scale prediction problems. Interestingly, for binary data, b-bit miwise hashing is substantially much more accurate than other popular methods such as random projections. Experimental results on 200GB data (in billion dimensions) will also be presented.

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