LSI就是这样一种维数约减方法。它可以通过对“文档向量矩阵”进行解奇异值分解（SVD: Singular Value Decomposition）运算，自动计算得到一个比原始空间小得多的有效语义空间-LSI is such a dimensionality reduction method. It can be through the "document vector matrix" for the singular value decomposition (SVD: Singular Valu

QR算法计算一个矩阵的Schur分解。这当然是一个 在特征值的计算中最重要的算法 -QR algorithm to compute the Schur decomposition of a matrix. This is certainly a feature in the calculation of the value of the most important algorithms

矩阵的QR分解定理: 任意nxn实矩阵A, 存在正交阵Q与上三角阵R, 使得A=Q*R-QR matrix decomposition theorem: any nxn real matrix A, the presence of orthogonal matrix Q and the upper triangular matrix R, such that A = Q* R