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Kegl, B. and Linder, T. (2000) Learning and Design of Principal Curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 281-297.

has been cited by the following article:

• TITLE: An Actual Survey of Dimensionality Reduction

  AUTHORS: Alireza Sarveniazi

  KEYWORDS: Dimensionality Reduction Methods

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.4 No.2, March 21, 2014

  ABSTRACT: Dimension reduction is defined as the processes of projecting high-dimensional data to a much lower-dimensional space. Dimension reduction methods variously applied in regression, classification, feature analysis and visualization. In this paper, we review in details the last and most new version of methods that extensively developed in the past decade.