## Abstract

Given a set of n vectors in R^{m} 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 the bounded coefficients criterion, while the determinant of the selected set is at most m^{m/2} below the true maximum determinant.

Original language | English |
---|---|

Pages | 102-105 |

Number of pages | 4 |

State | Published - 2004 |

Event | 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel Duration: 6 Sep 2004 → 7 Sep 2004 |

### Conference

Conference | 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings |
---|---|

Country/Territory | Israel |

City | Tel-Aviv |

Period | 6/09/04 → 7/09/04 |