Robust hypersurface fitting based on random sampling approximations

Jun Fujiki, Shotaro Akaho, Hideitsu Hino, Noboru Murata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper considers N - 1-dimensional hypersurface fitting based on L 2 distance in N-dimensional input space. The problem is usually reduced to hyperplane fitting in higher dimension. However, because feature mapping is generally a nonlinear mapping, it does not preserve the order of lengthes, and this derives an unacceptable fitting result. To avoid it, JNLPCA is introduced. JNLPCA defines the L 2 distance in the feature space as a weighted L 2 distance to reflect the metric in the input space. In the fitting, random sampling approximation of least k-th power deviation, and least α-percentile of squares are introduced to make estimation robust. The proposed hypersurface fitting method is evaluated by quadratic curve fitting and quadratic curve segments extraction from artificial data and a real image.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
ページ520-527
ページ数8
PART 3
DOI
出版ステータスPublished - 2012
イベント19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
継続期間: 2012 11 122012 11 15

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 3
7665 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
国/地域Qatar
CityDoha
Period12/11/1212/11/15

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

フィンガープリント

「Robust hypersurface fitting based on random sampling approximations」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル