For improving forecast accuracy and the efficiency of network training, we USES principle components analysis method to reduce the dimensionality of the feature space.
为了进一步提高预测精度和网络训练的效率，对众多的预报因子采用主成分分析的方法进行降维处理。
The model selection principle of determining effective number of dimensionality reduction for different clusters is proposed.
并提出了针对不同类簇判断有效降维维数的模型选择准则。
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