Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things

In the real world, the law data in the smart Internet of Things usually consists of heterogeneous information with some noises. Non-negative matrix factorization is a popular tool for multi-view learning, which can be employed to represent and learn heterogeneous law features comprehensively. However, current NMF-based techniques generally use clean multi-view datasets to generate common subspace, while in practice, they often contain some noises or unrelated items so that the performance of the algorithms may be severely degraded. In this paper, we propose to develop a novel subspace learning model, called Adaptive Dual Graph-regularized Multi-View Non-Negative Feature Learning (ADMFL), for multi-view data representation. We utilize the geometric structures of both data and feature manifold to model the distribution of data points in the common subspace. Meanwhile, we lift the effect of unrelated features down through separating the view-specific features for each view. Moreover, we introduce a weight factor for all views and maintain the sparsity of the latent common representation. An effective objective function is thus designed and iteratively updated until convergence. Experiments on standard datasets demonstrate that the proposed ADMFL method outperforms other compared methods in the paper. (C) 2019 Elsevier B.V. All rights reserved.