Fast Non-Linear Dimension Reduction
Abstract
We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Exper(cid:173) iments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train.
Cite
Text
Kambhatla and Leen. "Fast Non-Linear Dimension Reduction." Neural Information Processing Systems, 1993.Markdown
[Kambhatla and Leen. "Fast Non-Linear Dimension Reduction." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/kambhatla1993neurips-fast/)BibTeX
@inproceedings{kambhatla1993neurips-fast,
title = {{Fast Non-Linear Dimension Reduction}},
author = {Kambhatla, Nanda and Leen, Todd K.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {152-159},
url = {https://mlanthology.org/neurips/1993/kambhatla1993neurips-fast/}
}