Firstly to select the bands that have class separability by K2 algorithm, then remove the redundant bands based on conditional mutual information test.

使用K2结构学习算法选出具有类别可分性的波段，进一步利用互信息测试对遥感波段之间的相关性做分析，去除冗余信息。

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While introducing noise reduction evaluation criteria, which based on class separability measure for noise reduction, this paper evaluated the noise reduction effect of complex vibration signals.

同时引入基于类可分离性测度的降噪评价准则，实现了对复杂振动信号的降噪效果的评价。

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The algorithm proposed here not only imposes an uncorrelated constraint to reduce data redundancy, but also utilizes the class information and the interclass separability after projection is enhanced.

本文算法使得所提特征之间相互无关，这样降低了数据冗余，同时考虑到类别信息，使得投影后的类间区分度加强了。

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