连续安全的静态和动态选规则用于加速支张力机器
Hongmei Wang1, Kun Jiang2, Xiao Li3
1Business School, Shandong Normal University, Jinan 250014, China.
概括
本研究介绍了一种高效的顺序安全静态和动态选规则 (SS-SDSR),以加快支持 Tensor 机器 (STM) 训练. 该方法减少了冗余变量,而不会影响分类准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算统计学 计算统计学
背景情况:
- 支持张量机 (STM) 是有效的张量数据分类,保存结构和减轻维度.
- 传统的STM方法依赖于耗时的交替投影代技术.
研究的目的:
- 为加速STM提出一个有效的顺序安全静态和动态选规则 (SS-SDSR).
- 通过在训练过程中识别和删除冗余变量而减少计算成本,而不会损失准确性.
主要方法:
- 开发了基于变异不平等和二元差距的静态和动态选规则.
- 实施了一种连续的选过程,使用具有不同参数的静态规则和具有一致参数的动态规则.
主要成果:
- 人工数据集的实验表明SS-SDSR在适当的参数间隔和选频率的数据形式中是有效的.
- 在11个矢量和6个张量数据集上的数值实验表明SS-SDSR的可行性和有效性与其他五种算法相比.
结论:
- 拟议的SS-SDSR有效地加速了STM培训.
- 该方法是高效的,安全的,在各种数据集上表现出强的性能.
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