散布矩阵 meaning in Chinese
scattering matrix
Examples
- By constructing weighted between - class scatter matrix , the classes that are closer to one another are likely to have a greater confusion and should be given a greater weightage
该算法通过构建加权的类间散布矩阵，将距离较近的容易混淆的类别赋以较大的权值。 - A concise representation method of between - class scatter matrix and population scatter matrix is proposed , which suits for all the applications of pattern recognition using fisher criteria
提出了散布矩阵的一种简洁表示方法，这一简洁表示方法适合于一切使用fisher鉴别准则的模式识别问题。 - Firstly , to perform pca or lda on basis of such high - dimensional image vectors is a time - consuming process . secondly , the high dimensionality usually leads to singularity of the within - class covariance matrix , which is a trouble for calculation of fisher optimal discriminant vectors
这样就从根本上避免了在高维的图像向量空间内构造散布矩阵并计算特征向量的困难，大幅度地降低了特征抽取过程所耗费的计算量。 - Standard face images are formed through the above - mentioned processing . during the feature extraction , for those standard face images , regarding the between - class scatter matrix as generating matrix , we extract the algebraic features of face images through k - l transform and singular value decomposition
在人脸特征提取过程中，对经过预处理的标准人脸图像，以类间散布矩阵为产生矩阵，通过k - l变换降维并结合奇异值分解来提取人脸代数特征。 - The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors . that is to say , if we use them to deal with the image recognition problem , the first step is to transform original image matrices into same dimensional vectors , and then rely on these vectors to evaluate the covariance matrix and to determine the projector
所提出的这两种方法的共同特点是，在进行图像特征抽取时，不需要事先将图像矩阵转化为高维的图像向量，而是直接利用图像矩阵本身构造图像散布矩阵，然后基于这些散布矩阵进行主分量分析与线性鉴别分析。