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

机译：智能物联网中具有双图正则化的法律数据特征学习的无监督多视图非负数

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摘要

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.
机译：在现实世界中，智能物联网中的法律数据通常由具有某些噪声的异类信息组成。非负矩阵分解是一种用于多视图学习的流行工具，可用于全面表示和学习异类法律特征。但是，当前基于NMF的技术通常使用干净的多视图数据集来生成公共子空间，而在实践中，它们通常包含一些噪声或不相关的项，从而可能严重降低算法的性能。在本文中，我们建议开发一种新颖的子空间学习模型，称为自适应双图正则化多视图非负特征学习（ADMFL），用于多视图数据表示。我们利用数据和特征流形的几何结构对公共子空间中数据点的分布进行建模。同时，我们通过分离每个视图的特定于视图的特征来降低无关特征的影响。此外，我们为所有视图引入了权重因子，并保持了潜在共同表示的稀疏性。因此，设计了有效的目标函数并迭代更新，直到收敛为止。在标准数据集上进行的实验表明，提出的ADMFL方法优于本文中其他比较方法。 （C）2019 Elsevier B.V.保留所有权利。

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