The improved PCA is used to reduce the dimension first to get the optimal characteristic subspace of the original sample data set. In this process, the intra-class scatter matrix of the characteristic subspace should be ensured to be non-singular.
首先,用改进PCA算法对原样本降维,获得最优特征表示子空间;然后,在保证该子空间类内散度矩阵非奇异的基础上,作改进的线性判别分析。
Is proposed, which explicitly takes into account the matrix-based intra-class submanifold and inter-class submanifold by integrating the local intra-class compactness information and the non-local inter-class separability information.
它通过结合局部类内紧凑信息和非局部类间分离信息显式地考虑了基于矩阵的类内子流形和类间子流形。
And can design independently, development intra-class correlation product.
并能自主设计, 研发同类相关产品.
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