Limitations of Self-Organizing Maps for Vector Quantization and Multidimensional Scaling
Abstract
The limitations of using self-organizing maps (SaM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SaM 's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined tech(cid:173) nique of online K-means clustering plus Sammon mapping of the cluster centroids. SaM are shown to perform significantly worse in terms of quantization error , in recovering the structure of the clus(cid:173) ters and in preserving the topology in a comprehensive empirical study using a series of multivariate normal clustering problems.
Cite
Text
Flexer. "Limitations of Self-Organizing Maps for Vector Quantization and Multidimensional Scaling." Neural Information Processing Systems, 1996.Markdown
[Flexer. "Limitations of Self-Organizing Maps for Vector Quantization and Multidimensional Scaling." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/flexer1996neurips-limitations/)BibTeX
@inproceedings{flexer1996neurips-limitations,
title = {{Limitations of Self-Organizing Maps for Vector Quantization and Multidimensional Scaling}},
author = {Flexer, Arthur},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {445-451},
url = {https://mlanthology.org/neurips/1996/flexer1996neurips-limitations/}
}