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

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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.