ICML 2015poster3 citations
An Online Learning Algorithm for Bilinear Models
Abstract
We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a regret bound. Experiments on two sequential labelling tasks give positive results.
BibTeX
@InProceedings{pmlr-v37-wua15,
title = {An Online Learning Algorithm for Bilinear Models},
author = {Wu, Yuanbin and Sun, Shiliang},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {890--898},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/wua15.pdf},
url = {https://proceedings.mlr.press/v37/wua15.html},
abstract = {We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a regret bound. Experiments on two sequential labelling tasks give positive results.}
}