1) recency rank
最近秩
1.
This paper briefly introduces the principles of the recency rank coding and constructs the related codeset by using the inserting method.
简要给出了最近秩（ＲｅｃｅｎｃｙＲａｎｋ）编码原理，运用插值法构成了其编码码集，并采用位输入输出方法实现了该算法的压缩及还原。
2) low-rank approximation
低秩逼近
1.
As a technology for obtaining low rank denotation of extremely large matrix, Low-Rank Approximation plays an important role in many fields such as pattern recognition, machine learning and data mining.
低秩逼近是一种寻求大规摸矩阵的低秩近似表示技术,在模式识别,机器学习和数据挖掘等领域有着广泛的应用,是人们从复杂的数据中寻找有用信息的强有力的方法。
3) Low-rank approxiamtion
低秩近似
4) rank-r approximation
秩-r逼近
5) minimal rank
最小秩
1.
In this paper,we give several properties of the minimal rank of matrices over any number field and characterize linear mapping of keeping the minimal rank.
本文给出任意数域上n×n阵的最小秩的几个性质,并且刻画保最小秩的线性映射。
2.
In this paper,the properties of the srew-idempotent matrix are presented by using the minimal rank of the complement Schur.
利用广义Schur补D-CA-B的最小秩给出斜幂等矩阵的一些性质。
3.
In the paper the maximal and the minimal rank of the matrix expression G+H-G(A+B)H are given by applying the rank of the generalized Schur complement of the matrix,where G,H are respectively the generalized inverse or weight generalized inverse of the matrix A,B.
利用矩阵的广义Schur补的秩给出矩阵表达式G+H-G(A+B)H的最大最小秩,其中G,H分别为矩阵A和B的广义逆,进而给出A,B关于广义逆的吸收律成立的等价条件。
6) maximal rank
最大秩
1.
In this pa- per we get the equivalent conditions for {A~-B~-}■{(AB)~-} by the maximal rank of matrix expression.
本文利用矩阵表达式最大秩方法获得了{A~-B~-}■{(AB)~-}的等价性条件,并讨论了{A~-B~-}■{(AB)~-}与{B~-A~-}■{(AB)~-}的关系。
补充资料：誾誾秩秩
1.人才众多貌。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条