1) Kernel Uncorrelated Discriminant Subspace(KUDS)
核不相关辨别子空间
1.
A Kernel Uncorrelated Discriminant Subspace(KUDS) method based on Generalized Singular Value Decomposition(GSVD) for radar target recognition is proposed.
该文提出了一种基于广义奇异值分解的核不相关辨别子空间算法,并将其用于高分辨距离像雷达目标识别。
3) kernel uncorrelated nonlinear discriminant analysis
核非线性不相关辨别分析
4) spatial correlation kernel
空间相关核
1.
The local features in the image are extracted and quantized,and the spatial location auto-correlations are calculated for vector-quantized local features,and then the histogram intersection is used to match spatial location auto-correlations of two images to obtain the local feature spatial correlation kernel.
为了描述局部特征在图像空间中相对位置关系,提出一种局部特征空间相关核(Spatial Correlation Kernel,SCK)用于图像目标分类。
5) double discriminant subspaces
双辨别子空间
1.
High resolution range profile radar target recognition based on double discriminant subspaces algorithm
双辨别子空间高分辨距离像雷达目标识别
补充资料：非线性相关
如果两种相关现象之间并不表现为直线的关系，而是近似于某种曲线方程的关系，则这种相关关系称为非线性相关。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条