Unsupervised Ensemble Learning Via Network Generation.

In this work, we propose an unsupervised ensemble learning method via network generation, referred to as UELNG. Specifically, we first generate weights of clustering ensemble models by adopting HyperGAN, and obtain diverse partitions for data. With these partitions, we can easily identify high-confident pseudo-labels as supervised information to predict low-entropy labels for unlabeled augmented data, thereby enhancing the quality of pseudo-labels and clustering accuracy. We conduct experiments on multiple data sets. Experimental results indicate that our method outperforms state-of-the-art methods by 0.3%, 1.8%, 5.7%, 3.2% and 2.4% on MNIST, STL-10, CIFAR-10, Reuters and 20News, respectively, which demonstrates the effectiveness of our proposed UELNG.