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Activity Number:
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80
- Sufficient Dimension Reduction and Applications
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #317118
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Title:
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Real-Time Sufficient Dimension Reduction Through Principal Least-Squares Support-Vector Machines
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Author(s):
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Yuexiao Dong* and Andreas Artemiou and Seung Jun Shin
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Companies:
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Temple University and Cardiff University and Korea University
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Keywords:
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central subspace;
ladle estimator;
online sliced inverse regression;
principal support-vector machines;
streamed data
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Abstract:
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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, our 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.
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Authors who are presenting talks have a * after their name.
