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Sign Cauchy Projections and Chi-Square Kernel
Ping Li · Gennady Samorodnitsk · John Hopcroft

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None
The method of Cauchy random projections is popular for computing the $l_1$ distance in high dimension. In this paper, we propose to use only the signs of the projected data and show that the probability of collision (i.e., when the two signs differ) can be accurately approximated as a function of the chi-square ($\chi^2$) similarity, which is a popular measure for nonnegative data (e.g., when features are generated from histograms as common in text and vision applications). Our experiments confirm that this method of sign Cauchy random projections is promising for large-scale learning applications. Furthermore, we extend the idea to sign $\alpha$-stable random projections and derive a bound of the collision probability.

Author Information

Ping Li (Baidu Research USA)
Gennady Samorodnitsk (Cornell University)
John Hopcroft (Cornell University)

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