Large Margin Non-Linear Embedding
Abstract
It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, we "learn" the location of the data. This way we (i) do not need a metric (or even stronger structure) - pairwise dissimilarities suffice; and additionally (ii) produce low-dimensional embeddings that can be analyzed visually. We achieve this by combining an entropy-based embedding method with an entropy-based version of semi-supervised logistic regression. We present results for clustering and semi-supervised classification.
Cite
Text
Zien and Candela. "Large Margin Non-Linear Embedding." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102485Markdown
[Zien and Candela. "Large Margin Non-Linear Embedding." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/zien2005icml-large/) doi:10.1145/1102351.1102485BibTeX
@inproceedings{zien2005icml-large,
title = {{Large Margin Non-Linear Embedding}},
author = {Zien, Alexander and Candela, Joaquin Quiñonero},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {1060-1067},
doi = {10.1145/1102351.1102485},
url = {https://mlanthology.org/icml/2005/zien2005icml-large/}
}