Reduction is used to decrease the dimension of structured data and the various compact degrees of data sets are obtained.
通过约简以减少结构化数据的维数，获得数据集合的不同简洁程度表示已成为数据挖掘的重要任务之一。
Fuzzy rough set theory is an effective tool for reduction of data dimension, but there are few dimension reduction algorithms that are based on fuzzy rough set theory so far.
模糊粗糙集理论是解决数据集维数问题的有效工具，但基于模糊粗糙集的降维算法还不多。
Singular Value Decomposition (SVD) is a dimension reduction method, and Symbolic data Analysis (SDA) is a new analytical approach to processing mass data.
奇异值分解(SVD)是一种对数据进行降维处理的方法，符号数据分析(SDA)是一种处理海量数据的全新数据分析思路。
An example presented in the paper demonstrates that this reduction of dimension makes the high-resolution inversion of the normal well logging data possible.
实例计算表明，这种降维方法使测井资料高分辨率反演成为可能。
To reduce the complexity, dimension reduction executes before ICA algorithm, and then after iteration of the orthogonal data processing.
为了降低复杂度，在进行ICA运算时，先对接收数据进行降维预处理，然后对迭代后的数据进行正交化处理。
The dimension reduction of hyperspectral data was classified by SVM.
对降维后的高光谱数据采用SVM进行分类。
The dimension reduction of hyperspectral data was classified by SVM.
对降维后的高光谱数据采用SVM进行分类。
应用推荐