Training sample set inevitably contains gross error in input signal reconstruction of nonlinear multifunctional sensor.
在非线性多功能传感器的信号重构过程中，训练样本集不可避免地夹杂粗差数据。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
The results of simulation experiments show effective reduction for large-scale training sample set and improvement of operation efficiency of this algorithm, guaranteeing the classification precision.
仿真实验表明，该算法能在保证不降低分类精度的前提下，对较大规模的样本进行有效的缩减，提高运算效率。
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