As relevant technologies advance, streamed data are frequently encountered in various
applications, and the need for scalable algorithms becomes urgent. In this article, we …

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

Compared with relatively easy feature creation or generation in data analysis, manual data
labeling needs a lot of time and effort in most cases. Even if automated data labeling​ …

Following recent developments of dimension reduction algorithms for a multivariate time
series, we propose in this work the adaptation of sliced inverse mean difference algorithm …

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 …

There is an ever-growing emphasis on mental health with greater demands for already
sparse mental health professionals. Applying machine learning as another criterion to better …

Envelope methodology is a promising dimension reduction approach. It was introduced in
the regression framework. In this work, we extended envelope application and focused on …