Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation
Abstract
Traditional graph-based semi-supervised learning (SSL) approaches are not suited for massive data and large label scenarios since they scale linearly with the number of edges |E| and distinct labels m. To deal with the large label size problem, recent works propose sketch-based methods to approximate the label distribution per node thereby achieving a space reduction from O(m) to O(\log m), under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that effectively captures the sparsity of the label distribution and further reduces the space complexity per node to O(1). We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. Finally, we propose a robust graph augmentation strategy using unsupervised deep learning architectures that yields further significant quality gains for SSL in natural language applications.
BibTeX
@InProceedings{pmlr-v51-ravi16,
title = {Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation},
author = {Ravi, Sujith and Diao, Qiming},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {519--528},
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/ravi16.pdf},
url = {https://proceedings.mlr.press/v51/ravi16.html},
abstract = {Traditional graph-based semi-supervised learning (SSL) approaches are not suited for massive data and large label scenarios since they scale linearly with the number of edges |E| and distinct labels m. To deal with the large label size problem, recent works propose sketch-based methods to approximate the label distribution per node thereby achieving a space reduction from O(m) to O(\log m), under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that effectively captures the sparsity of the label distribution and further reduces the space complexity per node to O(1). We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. Finally, we propose a robust graph augmentation strategy using unsupervised deep learning architectures that yields further significant quality gains for SSL in natural language applications.}
}