Self Basis Selection in a Finite Set

机译：有限集合中的自我基础选择

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Given a set of n vectors in R~m, m < n, we wish to find a subset of m vectors that are good "predictors" for the complementary set. We consider two criteria of goodness, one leads to requiring that the least-squares expansion coefficients of the complementary set be bounded by one, the other leads to maximizing the determinant of the selected subset. Exhaustive search requires checking all n choose m possible subsets. We present a low-complexity iterative selection algorithm, and examine its worst loss with respect to the optimum solution under both goodness criteria. We show that with linear complexity in n, the proposed algorithm achieves expansion coefficients which are uniformly bounded by 1 + ε, while the determinant of the selected subset is at most m~(m/2) below the true maximum determinant.

机译：给定Rm中的n个向量的集合，m

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《Annual Allerton Conference on Communication, Control, and Computing; 20040929-1001; Monticello,IL(US)》|2004年|P.634-643|共10页
会议地点 MonticelloIL(US)
作者
Ishai Hani; Ram Zamir;
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作者单位

Actelis Networks, Israel;

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原文格式 PDF
正文语种 eng
中图分类通信;
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Self Basis Selection in a Finite Set

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