This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.
应用特征选取和模式聚合理论以降低特征空间维数。
Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.
文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。
First, based on the idea of "dimension" from psychology, an orthogonal common emotion space is constructed. Then, sensitive features are extracted from images to construct the feature space.
同时，抽取图像中较容易引起情感变化的特征作为图像的视觉特征，建立图像的特征空间；
The transformation of kernel function is used to solve the overlapped mixed gas feature absorption spectrum in high dimension space.
算法采用核函数变换的方式，将重叠严重和非线性的光谱数据进行高维空间变换后再计算各组分气体浓度。
The distinguished feature of the present algorithm is its computation time and space independent of the dimension of the windows.
本文算法的最大特点在于计算速度与运算空间不随计算窗口的变化而变化。
Numerical simulation shows that the proposed method can reduce the dimension of the feature space, and has higher correct classification rate.
数值实验表明，该方法可以降低特征空间维数，具有较好的分类准确率。
In order to find out the feature patterns from multi-dimension space, the conventional approach is to separate feature space by assuming the distributed functions of all features in one time.
对于在特征空间中寻找特征模式，一般是通过假设分布函数一次性对样本空间进行分离的方法去试图获得特征空间的样本总体分布规律。
In order to find out the feature patterns from multi-dimension space, the conventional approach is to separate feature space by assuming the distributed functions of all features in one time.
对于在特征空间中寻找特征模式，一般是通过假设分布函数一次性对样本空间进行分离的方法去试图获得特征空间的样本总体分布规律。
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