论文标题
干涉图转换：深度无监督的图表
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
论文作者
论文摘要
我们提出了干涉图转换（IGT），这是用于构建图表表示的新的深度无监督图卷积神经网络。我们的第一个贡献是提出从欧几里得傅里叶变换的概括获得的通用，复杂值的光谱图结构。我们表明，由于一个新颖的贪婪的凹面目标，我们所学的表示既包括歧视性和不变特征。从我们的实验中，我们得出的结论是，我们的学习过程利用了光谱域的拓扑，这通常是光谱方法的瑕疵，尤其是我们的方法可以恢复视力任务的分析操作员。我们在各种挑战性的任务上测试算法，例如图像分类（MNIST，CIFAR-10），社区检测（作者，Facebook图）以及3D骨骼视频（SBU，NTU）的动作识别，在光谱图中展示了不耐磨的设置中的新的最新目的。
We propose the Interferometric Graph Transform (IGT), which is a new class of deep unsupervised graph convolutional neural network for building graph representations. Our first contribution is to propose a generic, complex-valued spectral graph architecture obtained from a generalization of the Euclidean Fourier transform. We show that our learned representation consists of both discriminative and invariant features, thanks to a novel greedy concave objective. From our experiments, we conclude that our learning procedure exploits the topology of the spectral domain, which is normally a flaw of spectral methods, and in particular our method can recover an analytic operator for vision tasks. We test our algorithm on various and challenging tasks such as image classification (MNIST, CIFAR-10), community detection (Authorship, Facebook graph) and action recognition from 3D skeletons videos (SBU, NTU), exhibiting a new state-of-the-art in spectral graph unsupervised settings.
