Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

April 1, 1999, Vol. 11, No. 3, Pages 595-600
(doi: 10.1162/089976699300016584)
© 1999 Massachusetts Institute of Technology
Improved Multidimensional Scaling Analysis Using Neural Networks with Distance-Error Backpropagation
Article PDF (68.45 KB)
Abstract

We show that neural networks, with a suitable error function for back-propagation, can be successfully used for metric multidimensional scaling (MDS) (i.e., dimensional reduction while trying to preserve the original distances between patterns) and are in fact able to outdo the standard algebraic approach to MDS, known as classical scaling.