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中文摘要：
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．
英文摘要：
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.
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