期刊导航

论文摘要

双变换算法在多维序列数据分析中的优化研究

Research on optimization of double transform algorithm in multidimensional sequence data analysis

作者:刘云(昆明理工大学信息工程与自动化学院);易松(昆明理工大学信息工程与自动化学院)

Author:LIU Yun,(Faculty of Information Engineering and Automation, Kunming University of Science and Technology);Yi Song((Faculty of Information Engineering and Automation, Kunming University of Science and Technology)

收稿日期:2018-04-23          年卷(期)页码:2019,56(4):633-638

期刊名称:四川大学学报: 自然科学版

Journal Name:Journal of Sichuan University (Natural Science Edition)

关键字:降维;序列数据;酉变换;双曲线旋转变换

Key words:Dimensionality reduction; Sequence data; Unitary transformation; Hyperbolic rotation transformation

基金项目:国家自然科学基金(61262040)

中文摘要

在流数据中,降低维度是处理多维序列数据的重要因素.提出一种双变换算法(DTA),针对在线序列数据,分别进行酉变换和双曲线旋转变换的双变换处理,得到假设函数的参数,通过牛顿算法迭代预测误差值,直到小于所预设的阈值,从而得到最优预测值.仿真结果表明,对比OGD和RON两种算法,DTA算法在保障算法稳定性的前提下,有效减少计算时间.

英文摘要

In streaming data, dimension reduction is an important factor in processing multidimensional sequence data. In this paper, a double transform algorithm (DTA) is proposed. For the online sequence data, unitary transformation and hyperbolic rotation transformation is carried out respectively in DTA,and the parameters of the hypothesis function are obtained. The error values are predicted by the Newton iterative algorithm, and the optimal prediction value is obtained until the error is less than the predefined threshold. The simulation results show that, compared with the two algorithms of OGD and RON, the DTA algorithm effectively reduces the computation time under the premise of ensuring the algorithm stability.

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