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Activity Number: 697
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #315374
Title: Metric Learning for Right-Censored Outcomes
Author(s): Daniel Conn* and Zhenqiu Liu and Christina M. Ramirez and Gang Li
Companies: UCLA and Cedars Sinai Medical Center and UCLA Fielding School of Public Health and UCLA Fielding School of Public Health
Keywords: Metric Learning ; Survival Analysis ; Kernel Regression ; Machine Learning ; Nonparametric Statistics ; Convex Optimization
Abstract:

Abstract: In this paper we adapt the metric learning methodology to censored outcomes. Metric learning is an extension of kernel regression designed to overcome the flaws of kernel regression in moderate or high dimensions. In metric learning, the distance function is learned from the data via various optimization routines. This data adaptive distance function effectively down-weights unimportant features and up-weights important features. We demonstrate our method on Bioconductor's "curatedOvarianData."


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