基于非均匀采样的相关系数最大化曲线排齐方法  

Curve Registration Method for Maximizing Correlation Coefficient Based on Non-uniform Sampling

作　　者：张文凯[1] 王文剑[1,2] 姜高霞[1] 

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]山西大学计算智能与中文信息处理教育部重点实验室,太原030006

出　　处：《模式识别与人工智能》2016年第1期72-81,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61273291);山西省回国留学人员科研项目(No.2012-008)资助~~

摘　　要：在函数型数据分析中,为提高曲线排齐效率,提出如下2种非均匀采样方法对函数曲线进行排齐:基于斜率的非均匀采样(SBNS)和基于弧长的非均匀采样(ALBNS).SBNS按照函数曲线的斜率大小采样,ALBNS在函数曲线的弧长上采样.这两种方法都不是在时间轴上均匀采样,而是根据曲线的形状特征进行采样,因此可在一定程度上克服均匀采样方法由于采样点数量和位置分配不当而产生的缺陷,提高曲线排齐效果.在模拟数据和真实数据上的实验表明,两种方法在时间效率和效果上均优于均匀采样方法.In functional data analysis, two kinds of non-uniform sampling methods for curve registration are put forward to improve the efficiency. Slope based non-uniform sampling （SBNS） method samples according to the slope size of the function curve. Arc length based non-uniform sampling （ALBNS） method samples evenly in the arc length of function curve. Two non-uniform sampling methods sample according to characteristics of curves instead of sampling evenly in the time axis. Thus, the defects of uniform sampling method caused by the number and the location distribution of sample points are overcome and the effect of curve registration is improved. The experimental results on simulated data and real data show that the above two kinds of methods are better than uniform sampling method in time efficiency and the effect of curve registration.

关 键 词：函数型数据 曲线排齐 非均匀采样 斜率 弧长 

分 类 号：O211.61[理学—概率论与数理统计]