Title: SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning,
Abstract: It is known that Boosting can be interpreted as a gradient descent technique
to minimize an underlying loss function. Specifically, the underlying loss
being minimized by the traditional AdaBoost is the exponential loss, which is
proved to be very sensitive to random noise/outliers. Therefore, several
Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to
improve the robustness of AdaBoost by replacing the exponential loss with some
designed robust loss functions. In this work, we present a new way to robustify
AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning
(SPL) into Boosting framework. Specifically, we design a new robust Boosting
algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented
by slightly modifying off-the-shelf Boosting packages. Extensive experiments
and a theoretical characterization are also carried out to illustrate the
merits of the proposed SPLBoost.