A Discriminative Latent Variable Model for Online Clustering

Rajhans Samdani, Kai-Wei Chang, Dan Roth
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):1-9, 2014.

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 this Paper

BibTeX
@InProceedings{pmlr-v32-samdani14, title = {A Discriminative Latent Variable Model for Online Clustering}, author = {Samdani, Rajhans and Chang, Kai-Wei and Roth, Dan}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1--9}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/samdani14.pdf}, url = {https://proceedings.mlr.press/v32/samdani14.html}, 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.} }
Endnote
%0 Conference Paper %T A Discriminative Latent Variable Model for Online Clustering %A Rajhans Samdani %A Kai-Wei Chang %A Dan Roth %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-samdani14 %I PMLR %P 1--9 %U https://proceedings.mlr.press/v32/samdani14.html %V 32 %N 1 %X 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.
RIS
TY - CPAPER TI - A Discriminative Latent Variable Model for Online Clustering AU - Rajhans Samdani AU - Kai-Wei Chang AU - Dan Roth BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-samdani14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 1 SP - 1 EP - 9 L1 - http://proceedings.mlr.press/v32/samdani14.pdf UR - https://proceedings.mlr.press/v32/samdani14.html AB - 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. ER -
APA
Samdani, R., Chang, K. & Roth, D.. (2014). A Discriminative Latent Variable Model for Online Clustering. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(1):1-9 Available from https://proceedings.mlr.press/v32/samdani14.html.

Related Material