Semi-Supervised Learning and Feature Fusion for Multi-view Data Clustering

Generative Adversarial Networks GANs have become widely used in Single-view classification tasks. Nowadays, most of the data have multiple views and each view emphasizes a unique feature set of the data. In this paper, we investigate the application of GANs on Multi-view data for the task of clustering and few-shot learning. We propose mvSGAN, a deep learning approach to GAN multi-view clustering, where generator and classifier networks are in a competitive min-max game. A multi-view learning algorithm is implemented with a mini-batch which can handle large data sets. We test the accuracy of our method in clustering real-world data sets. The experimental results show that our method outperforms state-of-the-art research.