Generalized Low Rank Approximations of Matrices
Generalized Low Rank Approximations of Matrices
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DOI：
10.1007/s10994-005-3561-6
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发表时间：
2004-07
期刊：
影响因子：
7.5
通讯作者：
Jieping Ye
中科院分区：
文献类型：
--
作者：
Jieping Ye
我们考虑计算矩阵的低秩近似的问题。我们方法的新颖之处在于低秩近似位于矩阵序列上。与过去深入研究的单个矩阵的低秩近似问题不同，本文提出的算法一般不承认封闭形式的解。我们对人脸图像数据进行了广泛的实验，以评估所提出算法的有效性，并将计算出的低秩近似与传统的基于奇异值分解的方法获得的近似进行比较。
We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank approximations are on a sequence of matrices. Unlike the problem of low rank approximations of a single matrix, which was well studied in the past, the proposed algorithm in this paper does not admit a closed form solution in general. We did extensive experiments on face image data to evaluate the effectiveness of the proposed algorithm and compare the computed low rank approximations with those obtained from traditional Singular Value Decomposition based method.