Fast Semi-Supervised Learning on Large Graphs: An Improved Green-Function Method

Feiping Nie, Yitao Song, Wei Chang, Rong Wang, Xuelong Li

科研成果: 期刊稿件文章同行评审

摘要

In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied to large graphs, especially those sparse ones, this method performs unstably and unsatisfactorily. We make a detailed analysis on it and propose a novel method from the perspective of optimization. On fully connected graphs, the method is equivalent to the Green-function method and can be seen as another interpretation with physical meanings, while on non-fully connected graphs, it helps to explain why the Green-function method causes a mess on large sparse graphs. To solve this dilemma, we propose a workable approach to improve our proposed method. Unlike the original method, our improved method can also apply two accelerating techniques, Gaussian Elimination, and Anchored Graphs to become more efficient on large graphs. Finally, the extensive experiments prove our conclusions and the efficiency, accuracy, and stability of our improved Green's function method.

源语言英语
页(从-至)2055-2070
页数16
期刊IEEE Transactions on Pattern Analysis and Machine Intelligence
47
3
DOI
出版状态已出版 - 2025

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