Dimensionality reduction for semi-supervised face recognition

Weiwei Du, Kohei Inoue, Kiichi Urahama

研究成果: ジャーナルへの寄稿Conference article

5 引用 (Scopus)

抜粋

A dimensionality reduction technique is presented for semi-supervised face recognition where image data are mapped into a low dimensional space with a spectral method. A mapping of learning data is generalized to a new datum which is classified in the low dimensional space with the nearest neighbor rule. The same generalization is also devised for regularized regression methods which work in the original space without dimensionality reduction. It is shown with experiments that the spectral mapping method outperforms the regularized regression. A modification scheme for data similarity matrices on the basis of label information and a simple selection rule for data to be labeled are also devised.

元の言語英語
ページ(範囲)1-10
ページ数10
ジャーナルLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
3614
発行部数PART II
出版物ステータス出版済み - 10 27 2005
イベントSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, 中国
継続期間: 8 27 20058 29 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

フィンガープリント Dimensionality reduction for semi-supervised face recognition' 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