Cluster Kernels for Semi-Supervised Learning
Abstract
We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.
Cite
Text
Chapelle et al. "Cluster Kernels for Semi-Supervised Learning." Neural Information Processing Systems, 2002.Markdown
[Chapelle et al. "Cluster Kernels for Semi-Supervised Learning." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/chapelle2002neurips-cluster/)BibTeX
@inproceedings{chapelle2002neurips-cluster,
title = {{Cluster Kernels for Semi-Supervised Learning}},
author = {Chapelle, Olivier and Weston, Jason and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {601-608},
url = {https://mlanthology.org/neurips/2002/chapelle2002neurips-cluster/}
}