As an effective manifold-learning method, the local tangent space alignment (LTSA) algorithm is sensitive to outliers.
局部切空间排列(LTSA)算法是一种有效的流形学习方法，但该算法对孤立点的存在非常敏感。
In this paper, Unscented Transformation (UT) method is applied to space alignment problem, and different UT sampling strategies are analyzed.
本文使用UT算法来处理空间配准问题，并针对不同的UT采样策略进行比较分析。
The paper focuses on the sensitivity of local tangent space alignment (LTSA) to outliers, and presents a robust local tangent space alignment (RLTSA) based on outlier detection.
研究局部切空间排列方法(LTSA)对离群点的敏感性，提出一种基于离群点检测的鲁棒局部切空间排列方法(RLTSA)。
Such alignment contains space alignment which converts the sensor information to the common coordinate system.
这种配准包括了空间配准。空间配准就是将各自传感器的信息匹配到共同坐标系统。
Such alignment contains space alignment which converts the sensor information to the common coordinate system.
这种配准包括了空间配准。空间配准就是将各自传感器的信息匹配到共同坐标系统。
应用推荐