论文标题
需求：深矩阵大约非线性分解，以识别人脑功能磁共振成像的元，规范和子空间模式
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain
论文作者
论文摘要
深度神经网络（DNN）已经成为一种至关重要的计算方法，可以揭示人脑中的空间模式。但是，利用DNN来检测功能磁共振信号中的空间模式：1）有三个主要缺点。这是一个完全连接的体系结构，它增加了网络结构的复杂性，难以优化和容易拟合过度； 2）。大型训练样本的要求导致擦除特征提取中的个体/次要模式； 3）。需要手动调整超参数，这很耗时。因此，我们提出了一个新型的深层非线性基质分解，称为“深矩阵”，大约在这项工作中近似非线性分解（需求），以利用浅线性模型，例如稀疏字典学习（SDL）和DNNS。首先，提议的需求采用了非连接和多层堆叠的架构，与规范DNN相比，它更容易优化。此外，由于有效的体系结构，培训需求可以避免过度拟合，并可以根据小型数据集（例如单个数据）识别单个/次要功能；最后，引入了一种新型的等级估计器技术，以自动调整所有需求的所有超级参数。此外，提出的需求通过人脑中的实际功能磁共振成像数据通过其他四个同行方法来验证。简而言之，验证结果表明，与其他同伴方法相比，需求可以揭示人脑的可再现元，规范和亚空间特征。
Deep Neural Networks (DNNs) have already become a crucial computational approach to revealing the spatial patterns in the human brain; however, there are three major shortcomings in utilizing DNNs to detect the spatial patterns in functional Magnetic Resonance Signals: 1). It is a fully connected architecture that increases the complexity of network structures that is difficult to optimize and vulnerable to overfitting; 2). The requirement of large training samples results in erasing the individual/minor patterns in feature extraction; 3). The hyperparameters are required to be tuned manually, which is time-consuming. Therefore, we propose a novel deep nonlinear matrix factorization named Deep Matrix Approximately Nonlinear Decomposition (DEMAND) in this work to take advantage of the shallow linear model, e.g., Sparse Dictionary Learning (SDL) and DNNs. At first, the proposed DEMAND employs a non-fully connected and multilayer-stacked architecture that is easier to be optimized compared with canonical DNNs; furthermore, due to the efficient architecture, training DEMAND can avoid overfitting and enables the recognition of individual/minor features based on a small dataset such as an individual data; finally, a novel rank estimator technique is introduced to tune all hyperparameters of DEMAND automatically. Moreover, the proposed DEMAND is validated by four other peer methodologies via real functional Magnetic Resonance Imaging data in the human brain. In short, the validation results demonstrate that DEMAND can reveal the reproducible meta, canonical, and sub-spatial features of the human brain more efficiently than other peer methodologies.
