为了提高群体智能聚类算法的运行效率，提出了利用主成分分析改善模式投影时的随机性。
To improve the running efficiency of the algorithm, the stochastic mapping of the patterns was modified based on principal component analysis.
著名的线性变换的方法包括，例如，主成分分析，因子分析，投影寻踪。
Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit.
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
计算主成分的目的是将高维数据投影到较低维空间。
The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).
计算主成分的目的是将高维数据投影到较低维空间。
The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).
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