truncated singular value decomposition 截断奇异值分解 ; 奇异值分解
In this thesis, we choose truncated singular value decomposition to solve the resulting matrix equations, while the regularization parameter of TSVD is determined by the L-curve criterion.
鉴于此，必须采用正则化方法，本文中选用的是截断奇异值分解，其正则化参数用l -曲线准则来确定。
The first class of regular method is named stationary method which include two methods, truncated singular value decomposition (TSVD) method and truncated total least squares (TTLS) method.
第一类方法称为静态方法，主要包括截断奇异值分解(TSVD)方法和截断完全最小二乘(TTLS)方法。
In order to solve these problems, a rank-truncated multi-station TDOA localization algorithm based on singular value decomposition was presented.
为了解决这些问题，提出了一种基于奇异值分解的秩截短多站时差定位算法。
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