Meschach可以解稠密或稀疏线性方程组、计算特征值和特征向量和解最小平方问题，另外还有其它功能。
Meschach was designed to solve systems of dense or sparse linear equations, compute eigenvalues and eigenvectors, and solve least squares problems, among other things.
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.
提出重构样本库的概念及构建算法，获得稀疏样本库，减少特征向量维数。
To get sparse sample library and reduce eigenvector dimension, a new concept of reconstructing sample library and its corresponding algorithm are introduced and presented, respectively.
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