Scholkopf, B., Smola, A. and Müller, K.R. (1998) Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10, 1299-1319.
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.