Title :
Nonlinear dimensionality reduction with input distances preservation
Author :
Garrido, Lluís ; Gomez, Sergio ; Roca, Jaume
Author_Institution :
Dept. d´´Estructura i Constituents de la Materia, Barcelona Univ., Spain
Abstract :
A new error term for dimensionality reduction, which clearly improves the quality of nonlinear principal component analysis neural networks, is introduced, and some illustrative examples are given. The method maintains the original data structure by preserving the distances between data points
Keywords :
neural nets; data structure; input distances preservation; multidimensional data analysis; neural networks; nonlinear dimensionality reduction; principal component analysis;
Conference_Titel :
Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)
Conference_Location :
Edinburgh
Print_ISBN :
0-85296-721-7
DOI :
10.1049/cp:19991230
