Real-time sufficient dimension reduction through principal least squares support vector machines

We propose a real-time approach for sufficient dimension reduction. Compared with popular sufficient dimension reduction methods including sliced inverse regression and principal support vector machines, the proposed principal least squares support vector machines approach enjoys better estimation of the central subspace. Furthermore, this new proposal can be used in the presence of streamed data for quick real-time updates. It is demonstrated through simulations and real data applications that our proposal performs better and faster than existing algorithms in the literature. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The proposed real-time approach for sufficient dimension reduction, namely principal least squares support vector machines, provides better estimation of the central subspace compared to existing methods such as sliced inverse regression and principal support vector machines. This new proposal is also capable of quick real-time updates in the presence of streamed data, outperforming existing algorithms in terms of performance and speed.