Flexible Multi-View Unsupervised Graph Embedding

Abstract

Faced with the increasing data diversity and dimensionality, multi-view dimensionality reduction has been an important technique in computer vision, data mining and multi-media applications. Since collecting labeled data is difficult and costly, unsupervised learning is of great significance. Generally, it is crucial to explore the complementarity or independence of different feature spaces in multi-view learning. How to find a low-dimensional subspace to preserve the intrinsic structure of original unlabeled high-dimensional multi-view data is still challenging. In addition, noises and outliers always appear in real data. In this study, we propose a novel model called flexible multi-view unsupervised graph embedding (FMUGE). A flexible regression residual term is introduced so that the strict linear mapping is relaxed, new-coming data and noises are better handled, and the raw data negotiate with the learned low-dimensional representation in the procedure. To ensure the consistency among multiple views, FMUGE adaptively weights different features and fuses them to get an optimal multi-view consensus similarity graph, which assists high-quality graph embedding. We propose an efficient alternating iterative algorithm to optimize the proposed model. Finally, experimental results on synthetic and benchmark datasets show the significant improvement of FMUGE over the state-of-the-art methods.