truncated_singular_value_decomposition

1.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。
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2.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。
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3.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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