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Discriminative matrix-variate restricted Boltzmann machine classification model
Wireless Networks ( IF 3 ) Pub Date : 2020-01-02 , DOI: 10.1007/s11276-019-02234-w
Jinghua Li , Pengyu Tian , Dehui Kong , Lichun Wang , Shaofan Wang , Baocai Yin

Abstract

Matrix-variate Restricted Boltzmann Machine (MVRBM), a variant of Restricted Boltzmann Machine, has demonstrated excellent capacity of modelling matrix variable. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers must be added. In order to make the MVRBM itself be supervised, in this paper, we propose improved MVRBMs for classification, which can be used to classify 2D data directly and accurately. To this end, on one hand, classification constraint is added to MVRBM to get Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM). On the other hand, fisher discriminant analysis criterion for matrix-style variable is proposed and applied to the hidden variable, therefore, the extracted feature is more discriminative so as to enhance the classification performance of ClassMVRBM. We call the novel model Matrix-variate Restricted Boltzmann Machine Classification Model with Fisher discriminant analysis (ClassMVRBM-MVFDA). Experimental results on some publicly available databases demonstrate the superiority of the proposed models. Of which, the image classification accuracy of ClassMVRBM is higher than conventional unsupervised RBM, its variants and supervised Restricted Boltzmann Machine Classification Model (ClassRBM) for vector variable. Especially, the image classification accuracy of the proposed ClassMVRBM-MVFDA performs better than supervised ClassMVRBM and vectorial RBM-FDA.



中文翻译:

判别矩阵变量受限玻尔兹曼机分类模型

摘要

矩阵变量受限玻尔兹曼机(MVRBM)是受限玻尔兹曼机的一种变体,已展示出出色的建模矩阵变量的能力。但是,MVRBM仍然是无监督的生成模型,通常用于特征化或深度神经网络的初始化。使用MVRBM进行分类时,必须添加其他分类器。为了使MVRBM本身受到监督,我们提出了改进的MVRBM进行分类,可用于直接,准确地对2D数据进行分类。为此,一方面,将分类约束添加到MVRBM,以获得矩阵变量受限玻尔兹曼机器分类模型(ClassMVRBM)。另一方面,提出了针对矩阵式变量的Fisher判别分析准则,并将其应用于隐藏变量,因此,提取的特征更具判别性,从而提高了ClassMVRBM的分类性能。我们称其为具有Fisher判别分析的新型模型矩阵变量受限玻尔兹曼机器分类模型(ClassMVRBM-MVFDA)。在一些公共数据库上的实验结果证明了所提出模型的优越性。其中,ClassMVRBM的图像分类精度高于传统的非监督RBM,其变体和向量变量的监督受限玻尔兹曼机器分类模型(ClassRBM)。特别是,提出的ClassMVRBM-MVFDA的图像分类精度要优于监督的ClassMVRBM和矢量RBM-FDA。我们称其为具有Fisher判别分析的新型模型矩阵变量受限玻尔兹曼机器分类模型(ClassMVRBM-MVFDA)。在一些公共数据库上的实验结果证明了所提出模型的优越性。其中,ClassMVRBM的图像分类精度高于传统的非监督RBM,其变体和向量变量的监督受限玻尔兹曼机器分类模型(ClassRBM)。特别是,提出的ClassMVRBM-MVFDA的图像分类精度要优于监督的ClassMVRBM和矢量RBM-FDA。我们称其为具有Fisher判别分析的新型模型矩阵变量受限玻尔兹曼机器分类模型(ClassMVRBM-MVFDA)。在一些公共数据库上的实验结果证明了所提出模型的优越性。其中,ClassMVRBM的图像分类精度高于传统的非监督RBM,其变体和向量变量的监督受限玻尔兹曼机器分类模型(ClassRBM)。特别是,提出的ClassMVRBM-MVFDA的图像分类精度要优于监督的ClassMVRBM和矢量RBM-FDA。ClassMVRBM的图像分类精度高于传统的非监督RBM,其变体和向量变量的监督受限玻尔兹曼机器分类模型(ClassRBM)。特别是,提出的ClassMVRBM-MVFDA的图像分类精度要优于监督的ClassMVRBM和矢量RBM-FDA。ClassMVRBM的图像分类精度高于传统的非监督RBM,其变体和向量变量的监督受限玻尔兹曼机器分类模型(ClassRBM)。特别是,提出的ClassMVRBM-MVFDA的图像分类精度要优于监督的ClassMVRBM和矢量RBM-FDA。

更新日期:2020-01-04
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