Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

• URL: http://arxiv.org/abs/2212.01026v1
• Date: Fri, 2 Dec 2022 08:48:11 GMT
• Title: Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
• Authors: Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, Irwin King
• Abstract summary: We present a novel spectral feature argumentation for contrastive learning on graphs (and images) For each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power regime which enjoys two valuable byproducts (under mere one or two iterations) Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines.
• Score: 64.78221638149276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy. Thus, we present a novel spectral feature argumentation for contrastive learning on graphs (and images). To this end, for each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under mere one or two iterations): (i) it partially balances spectrum of the feature map, and (ii) it injects the noise into rebalanced singular values of the feature map (spectral augmentation). For two views, we align these rebalanced feature maps as such an improved alignment step can focus more on less dominant singular values of matrices of both views, whereas the spectral augmentation does not affect the spectral angle alignment (singular vectors are not perturbed). We derive the analytical form for: (i) the incomplete power iteration to capture its spectrum-balancing effect, and (ii) the variance of singular values augmented implicitly by the noise. We also show that the spectral augmentation improves the generalization bound. Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines, and is complementary with other augmentation strategies and compatible with various contrastive losses.

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