Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning
Abstract
In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clustering structure of a data graph near a prespecified "seed set" of nodes, or one might be interested in finding partitions in an image that are near a prespecified "ground truth" set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data.
Cite
Text
Hansen and Mahoney. "Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning." Journal of Machine Learning Research, 2014.Markdown
[Hansen and Mahoney. "Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning." Journal of Machine Learning Research, 2014.](https://mlanthology.org/jmlr/2014/hansen2014jmlr-semisupervised/)BibTeX
@article{hansen2014jmlr-semisupervised,
title = {{Semi-Supervised Eigenvectors for Large-Scale Locally-Biased Learning}},
author = {Hansen, Toke J. and Mahoney, Michael W.},
journal = {Journal of Machine Learning Research},
year = {2014},
pages = {3871-3914},
volume = {15},
url = {https://mlanthology.org/jmlr/2014/hansen2014jmlr-semisupervised/}
}