Empirical Inference
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.
| Author(s): | Zien, A. and Candela, JQ. |
| Links: | |
| Book Title: | ICML 2005 |
| Journal: | Proceedings of the 22nd International Conference on Machine Learning (ICML 2005) |
| Pages: | 1065-1072 |
| Year: | 2005 |
| Month: | August |
| Day: | 0 |
| Editors: | De Raedt, L. , S. Wrobel |
| Publisher: | ACM Press |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | New York, NY, USA |
| DOI: | 10.1145/1102351.1102485 |
| Event Name: | 22nd International Conference on Machine Learning |
| Event Place: | Bonn, Germany |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{3375,
title = {Large Margin Non-Linear Embedding},
journal = {Proceedings of the 22nd International Conference on Machine Learning (ICML 2005)},
booktitle = {ICML 2005},
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.},
pages = {1065-1072},
editors = {De Raedt, L. , S. Wrobel},
publisher = {ACM Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {New York, NY, USA},
month = aug,
year = {2005},
author = {Zien, A. and Candela, JQ.},
doi = {10.1145/1102351.1102485},
month_numeric = {8}
}