Efficient Preference Clustering via Random Fourier Features
Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks. Unlike approaches using the Nyström method, which randomly samples the training examples, we make use of random Fourier features, whose basis fun...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2019-09-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020003 |
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