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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation
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Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation

机译：半监控图规则化深NMF，具有用于数据表示的双正交约束

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摘要

Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled. NMF is particularly useful for dimensionality reduction of high-dimensional data. However, the mapping between the low-dimensional representation, learned by semi-supervised NMF, and the original high-dimensional data contains complex hierarchical and structural information, which is hard to extract by using only single-layer clustering methods. Therefore, in this article, we propose a new deep learning method, called semi-supervised graph regularized deep NMF with bi-orthogonal constraints (SGDNMF). SGDNMF learns a representation from the hidden layers of a deep network for clustering, which contains varied and unknown attributes. Bi-orthogonal constraints on two factor matrices are introduced into our SGDNMF model, which can make the solution unique and improve clustering performance. This improves the effect of dimensionality reduction because it only requires a small fraction of data to be labeled. In addition, SGDNMF incorporates dual-hypergraph Laplacian regularization, which can reinforce high-order relationships in both data and feature spaces and fully retain the intrinsic geometric structure of the original data. This article presents the details of the SGDNMF algorithm, including the objective function and the iterative updating rules. Empirical experiments on four different data sets demonstrate state-of-the-art performance of SGDNMF in comparison with six other prominent algorithms.
机译：半监督非负矩阵分解（NMF）利用NMF的强度有效地学习数据中包含的本地信息，并且当仅标记小数一小部分数据时也能够实现有效学习。 NMF特别适用于高维数据的维度降低。然而，通过半监控NMF学习的低维表示与原始高维数据之间的映射包含复分层和结构信息，这很难通过仅使用单层聚类方法来提取。因此，在本文中，我们提出了一种新的深度学习方法，称为半监控图，具有双正交约束（SGDNMF）的深度NMF。 sgdnmf了解从深度网络的隐藏层进行群集的表示，其中包含各种和未知属性。两个因子矩阵上的双正交约束被引入我们的SGDNMF模型，可以使解决方案独特并提高聚类性能。这改善了维度降低的效果，因为它只需要待标记的一小部分数据。此外，SGDNMF包含双超图拉普拉斯正则化，可以加强数据和特征空间中的高阶关系，并完全保留原始数据的内在几何结构。本文介绍了SGDNMF算法的详细信息，包括目标函数和迭代更新规则。与六种其他突出算法相比，四种不同数据集的实证实验表明了SGDNMF的最先进的性能。

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    Meng Yang; Shang Ronghua; Shang Fanhua; Jiao Licheng; Yang Shuyuan; Stolkin Rustam;

  • 作者单位

    Xidian Univ Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China|Xidian Univ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China;

    Xidian Univ Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China|Xidian Univ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China;

    Xidian Univ Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China|Xidian Univ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China;

    Xidian Univ Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China|Xidian Univ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China;

    Xidian Univ Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China|Xidian Univ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China;

    Univ Birmingham Extreme Robot Lab Birmingham B15 2TT W Midlands England;

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  • 正文语种 eng
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  • 关键词

    Matrix decomposition; Data mining; Dimensionality reduction; Linear programming; Deep learning; Laplace equations; Feature extraction; Bi-orthogonal constraints; deep non-negative matrix factorization (NMF); dimensionality reduction; dual-hypergraph Laplacian regularization; semi-supervised learning;

    机译：矩阵分解;数据挖掘;减少维度;线性规划;深度学习;拉普拉斯方程;特征提取;双正交约束;深度非负矩阵分解（NMF）;维度减少;二重尺寸拉普拉斯正规化;半监督学习;

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