AISTATS 2016poster2 citations
Semi-Supervised Learning with Adaptive Spectral Transform
Abstract
This paper proposes a novel nonparametric framework for semi-supervised learning and for optimizing the Laplacian spectrum of the data manifold simultaneously. Our formulation leads to a convex optimization problem that can be efficiently solved via the bundle method, and can be interpreted as to asymptotically minimize the generalization error bound of semi-supervised learning with respect to the graph spectrum. Experiments over benchmark datasets in various domains show advantageous performance of the proposed method over strong baselines.
BibTeX
@InProceedings{pmlr-v51-liu16,
title = {Semi-Supervised Learning with Adaptive Spectral Transform},
author = {Liu, Hanxiao and Yang, Yiming},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {902--910},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/liu16.pdf},
url = {https://proceedings.mlr.press/v51/liu16.html},
abstract = {This paper proposes a novel nonparametric framework for semi-supervised learning and for optimizing the Laplacian spectrum of the data manifold simultaneously. Our formulation leads to a convex optimization problem that can be efficiently solved via the bundle method, and can be interpreted as to asymptotically minimize the generalization error bound of semi-supervised learning with respect to the graph spectrum. Experiments over benchmark datasets in various domains show advantageous performance of the proposed method over strong baselines.}
}