Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

We propose a real-time approach for sufficient dimension reduction. Compared with popular
sufficient dimension reduction methods including sliced inverse regression and principal …

Sufficient dimension reduction is used pervasively as a supervised dimension reduction
approach. Most existing sufficient dimension reduction methods are developed for data with …

Principal support vector machine was proposed recently by Li, Artemiou and Li (2011) to
combine L1 support vector machine and sufficient dimension reduction. We introduce the …

In this paper, we propose a Support Vector Machine (SVM)-type algorithm, which is
statistically faster among other common algorithms in the family of SVM algorithms. The new …

In this paper, we introduce a sufficient dimension reduction (SDR) algorithm based on
distance‐weighted discrimination (DWD). Our methods is shown to be robust on the …

Li et al. presented the novel idea of using support vector machines (SVMs) to perform
sufficient dimension reduction. In this work, we investigate the potential improvement in …

We develop in this work a new dimension reduction method for high-dimensional settings.
The proposed procedure is based on a principal support vector machine framework where …

In this paper, we combine adaptively weighted large margin classifiers with Support Vector
Machine (SVM)-based dimension reduction methods to create dimension reduction methods …

In this work we try to address the imbalance of the number of points which naturally occurs
when slicing the response in Sufficient Dimension Reduction methods (SDR). Specifically …