A Discriminative Latent Variable Model for Online Clustering
Abstract
This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (L3M). We present an online clustering algorithm for L3M based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, L3 M outperforms several existing online as well as batch supervised clustering techniques.
Cite
Text
Samdani et al. "A Discriminative Latent Variable Model for Online Clustering." International Conference on Machine Learning, 2014.Markdown
[Samdani et al. "A Discriminative Latent Variable Model for Online Clustering." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/samdani2014icml-discriminative/)BibTeX
@inproceedings{samdani2014icml-discriminative,
title = {{A Discriminative Latent Variable Model for Online Clustering}},
author = {Samdani, Rajhans and Chang, Kai-Wei and Roth, Dan},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {1-9},
volume = {32},
url = {https://mlanthology.org/icml/2014/samdani2014icml-discriminative/}
}