### 抜粋

Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristic of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of principal components. In this paper, we reduce the dimensionality of image data with the proposed method, and examine its effectiveness in compression and recognition of the images.

元の言語 | English |
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ホスト出版物のタイトル | Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop |

編集者 | A. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas |

ページ | 589-598 |

ページ数 | 10 |

出版物ステータス | Published - 2004 |

イベント | Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis 継続期間: 2004 9 29 → 2004 10 1 |

### Other

Other | Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop |
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市 | Sao Luis |

期間 | 04/9/29 → 04/10/1 |

### ASJC Scopus subject areas

- Engineering(all)

## フィンガープリント A nonlinear principal component analysis on image data' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

## これを引用

Saegusa, R., Sakano, H., & Hashimoto, S. (2004). A nonlinear principal component analysis on image data. ： A. Barros, J. Principe, J. Larsen, T. Adali, & S. Douglas (版),

*Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop*(pp. 589-598)