Detail of Publication

Text Language | English |
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Authors | Masakazu Iwamura, Shinichiro Omachi, and Hirotomo Aso |

Title | Estimation of True Mahalanobis Distance from Eigenvectors of Sample Covariance Matrix |

Journal | Systems and Computers in Japan |

Vol. | 35 |

No. | 9 |

Pages | pp.30-38 |

Publisher | John Wiley & Sons, Inc. |

Reviewed or not | Not reviewed |

Month & Year | August 2004 |

Abstract | In statistical pattern recognition, the Bayesian decision theory gives a decision to minimize the expected probability of misclassification as long as the true distributions are given. However, in most practical situations, the true distributions are unknown, and the parameters of the distributions are usually estimated from training sample vectors. It is well-known that estimated parameters contain estimation errors when sample size is small, and the errors cause bad influence on recognition performance. Among the estimation errors of parameters, the estimation errors of eigenvectors have not been considered enough. In this paper, we present a method to estimate the true Mahalanobis distance from the sample eigenvectors (the eigenvectors of sample covariance matrix) by considering the estimation errors of eigenvectors. Recognition experiments show that the true Mahalanobis distance can be estimated, and better recognition accuracy is achieved by applying the proposed method without many training samples and any hyper-parameters. |

- Note

PDF file is available at http://www3.interscience.wiley.com/cgi-bin/jissue/109076289. - Entry for BibTeX
@Article{Iwamura2004, author = {Masakazu Iwamura and Shinichiro Omachi and Hirotomo Aso}, title = {Estimation of True Mahalanobis Distance from Eigenvectors of Sample Covariance Matrix}, journal = {Systems and Computers in Japan}, year = 2004, month = aug, volume = {35}, number = {9}, pages = {30--38}, publisher = {John Wiley \& Sons, Inc.} }