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Matrix variate deep belief networks with CP decomposition algorithm and its application
Multimedia Systems ( IF 3.9 ) Pub Date : 2020-07-03 , DOI: 10.1007/s00530-020-00666-5
Xiaogang Hou , Guanglei Qi

Deep belief networks (DBNs) are used in many applications such as image processing and pattern recognition. But the data vectorized resulting in the loss of high-dimensional data and valuable spatial information. The classical DBNs model is based on restricted Boltzmann machines (RBMs) and full connectivity between the visible units and the hidden units. It requires a large number of parameters to be trained using an army of training samples. However, it is difficult to obtain so many training samples in reality. To solve this problem, this paper proposes a matrix-variate deep belief networks (MVDBNs) model which is created from matrix variate RBMs whose parameter is restricted as canonical polyadic (CP) decomposition. MVDBNs are composed of two or more matrix-variate restricted Boltzmann machines (MVRBMs) whose input and latent variables are in matrix form. The MVDBNs have much fewer model parameters and deeper layer with better features to avoid overfitting and more accurate model easier to be learned. We demonstrate the capacity of MVDBNs on handwritten digit classification and medical image processing. We also extend MVRBMs to a multimodal case for image super-resolution.

中文翻译:

基于CP分解算法的矩阵变数深度置信网络及其应用

深度信念网络 (DBN) 用于许多应用程序,例如图像处理和模式识别。但是数据矢量化导致高维数据和有价值的空间信息丢失。经典 DBN 模型基于受限玻尔兹曼机 (RBM) 以及可见单元和隐藏单元之间的完全连接。它需要使用大量训练样本来训练大量参数。然而,现实中很难获得如此多的训练样本。为了解决这个问题,本文提出了一种矩阵变量深度置信网络 (MVDBNs) 模型,该模型由矩阵变量 RBM 创建,其参数限制为规范多元 (CP) 分解。MVDBN 由两个或多个矩阵变量受限玻尔兹曼机 (MVRBM) 组成,其输入和潜在变量采用矩阵形式。MVDBN 具有更少的模型参数和更深的层,具有更好的特征以避免过度拟合,并且更容易学习更准确的模型。我们展示了 MVDBN 在手写数字分类和医学图像处理方面的能力。我们还将 MVRBM 扩展到图像超分辨率的多模态案例。
更新日期:2020-07-03
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