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。
The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed.
所接收的混合信号首先被映射到高维的内核特征空间，和由内核特征空间上的适应度函数给出的特征矢量的基础构造。
In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.
该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。
In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.
该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。
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