Representation learning using deep random vector functional link networks for clustering

Abstract

Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised learning, including semi-supervised and multi-label. There are hardly any unsupervised research results for RVFLs. In this paper, we propose the unsupervised RVFL (usRVFL), and the unsupervised framework is generic that can be used with other RVFL variants, thus we extend it to an ensemble deep variant, unsupervised deep RVFL (usdRVFL). The unsupervised method is based on the manifold regularization while the deep variant is related to the consensus clustering method, which can increase the capability and diversity of RVFLs. Our unsupervised approaches also benefit from fast training speed, even the deep variant offers a very competitive computation efficiency. Empirical experiments on several benchmark datasets demonstrated the effectiveness of the proposed algorithms.