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Oct, 2021
使用随机矩阵减少机器学习的计算量:无性能损失的三元随机特征
Random matrices in service of ML footprint: ternary random features with no performance loss
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Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet
TL;DR
研究一种称为 Ternary Random Feature 的新型随机特征,在储存和计算效率方面比传统的随机傅里叶特征更优,同时在实验中表现优越,可以用在压缩/量化方法中。
Abstract
In this article, we investigate the
spectral behavior
of
random features
kernel matrices
of the type ${\bf K} = \mathbb{E}_{{\bf w}} \left
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