通过矩阵的初等变换可实现矩阵的满秩分解和强满秩矩阵的三角分解。
Full rank decomposition of matrix and triangular decomposition of strongly full rank can well be realized by elementary transformation method of matrix.
讨论了当矩阵A为满秩矩阵时求其广义逆的一种方法，并将此方法推广，给出当A为非满秩矩阵时求其广义逆的一般方法，同时给出算例。
This paper discusses the way about how to get the reflexive general inverse matrix of a full rank matrix A, and generalize this way, gives the general way for not full rank matrix.
摘要讨论了分块矩阵的广义逆，以及用矩阵的满秩子块表示广义逆。
The generalized inverse of partitioned matrices and the expression of generalized inverse using maximal nonsingular submatrix are discussed.
通常在使用递推加权最小二乘算法时，需要设计矩阵列满秩。
It is usually required that the design matrix is column full rank in applying recursive weighted least squares algorithm.
这些方法都是基于网关联矩阵的满秩分解。
And all of these approaches are derived from the full rank decomposition technique for the incidence matrix.
提出了一种新的病态混叠盲源分离算法。算法首先对观察信号进行预处理，把多余的观察信号剔除，使预处理后的混叠矩阵A是行满秩的；
It is proposed a new blind separation algorithm of ill-condition mixed sources. Observed signals are pre-processed through eliminating redundancy signals so that mixed matrix A is row full rank.
非负矩阵分解（NMF）要求分解得到的左矩阵为列满秩，这限制了它在欠定盲分离（UBSS）中的应用。
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS).
非负矩阵分解（NMF）要求分解得到的左矩阵为列满秩，这限制了它在欠定盲分离（UBSS）中的应用。
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS).
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