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。
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.
尽管数字模拟法适用范围广，但该方法的效率较低，尤其是针对高维和小失效概率问题。
Although numerical simulation method has wide applicability, its efficiency is low, especially for high dimensionality and small failure probability problems.
针对自适应随机测试(ART)存在的高维和距离度量问题，提出一种改进的软件自适应随机测试策略。
Adaptive Random Testing (ART) is an enhanced version of Random Testing (RT). There are two factors that restrict the performance of ART, high-dimension data and distance metric.
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