Improved Semi-Supervised Learning with Multiple Graphs
Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi
Abstract
We present a new approach for graph based semi-supervised learning based on a multi-component extension to the Gaussian MRF model. This approach models the observations on the vertices as jointly Gaussian with an inverse covariance matrix that is a weighted linear combination of multiple matrices. Building on randomized matrix trace estimation and fast Laplacian solvers, we develop fast and efficient algorithms for computing the best-fit (maximum likelihood) model and the predicted labels using gradient descent. Our model is considerably simpler, with just tens of parameters, and a single hyperparameter, in contrast with state-of-the-art approaches using deep learning techniques. Our experiments on benchmark citation networks show that the best-fit model estimated by our algorithm leads to significant improvements on all datasets compared to baseline models. Further, our performance compares favorably with several state-of-the-art methods on these datasets, and is comparable with the best performances.
BibTeX
@InProceedings{pmlr-v89-viswanathan19a,
title = {Improved Semi-Supervised Learning with Multiple Graphs},
author = {Viswanathan, Krishnamurthy and Sachdeva, Sushant and Tomkins, Andrew and Ravi, Sujith},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {3032--3041},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/viswanathan19a/viswanathan19a.pdf},
url = {https://proceedings.mlr.press/v89/viswanathan19a.html},
abstract = {We present a new approach for graph based semi-supervised learning based on a multi-component extension to the Gaussian MRF model. This approach models the observations on the vertices as jointly Gaussian with an inverse covariance matrix that is a weighted linear combination of multiple matrices. Building on randomized matrix trace estimation and fast Laplacian solvers, we develop fast and efficient algorithms for computing the best-fit (maximum likelihood) model and the predicted labels using gradient descent. Our model is considerably simpler, with just tens of parameters, and a single hyperparameter, in contrast with state-of-the-art approaches using deep learning techniques. Our experiments on benchmark citation networks show that the best-fit model estimated by our algorithm leads to significant improvements on all datasets compared to baseline models. Further, our performance compares favorably with several state-of-the-art methods on these datasets, and is comparable with the best performances.}
}