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.
Cite
Text
Liu and Yang. "Semi-Supervised Learning with Adaptive Spectral Transform." International Conference on Artificial Intelligence and Statistics, 2016.Markdown
[Liu and Yang. "Semi-Supervised Learning with Adaptive Spectral Transform." International Conference on Artificial Intelligence and Statistics, 2016.](https://mlanthology.org/aistats/2016/liu2016aistats-semi/)BibTeX
@inproceedings{liu2016aistats-semi,
title = {{Semi-Supervised Learning with Adaptive Spectral Transform}},
author = {Liu, Hanxiao and Yang, Yiming},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2016},
pages = {902-910},
url = {https://mlanthology.org/aistats/2016/liu2016aistats-semi/}
}