2 research outputs found
Safe Sample Screening for Robust Support Vector Machine
Robust support vector machine (RSVM) has been shown to perform remarkably
well to improve the generalization performance of support vector machine under
the noisy environment. Unfortunately, in order to handle the non-convexity
induced by ramp loss in RSVM, existing RSVM solvers often adopt the DC
programming framework which is computationally inefficient for running multiple
outer loops. This hinders the application of RSVM to large-scale problems. Safe
sample screening that allows for the exclusion of training samples prior to or
early in the training process is an effective method to greatly reduce
computational time. However, existing safe sample screening algorithms are
limited to convex optimization problems while RSVM is a non-convex problem. To
address this challenge, in this paper, we propose two safe sample screening
rules for RSVM based on the framework of concave-convex procedure (CCCP).
Specifically, we provide screening rule for the inner solver of CCCP and
another rule for propagating screened samples between two successive solvers of
CCCP. To the best of our knowledge, this is the first work of safe sample
screening to a non-convex optimization problem. More importantly, we provide
the security guarantee to our sample screening rules to RSVM. Experimental
results on a variety of benchmark datasets verify that our safe sample
screening rules can significantly reduce the computational time