论文标题
Delmar：深线性矩阵大约重建，以提取人脑中的层次功能连通性
DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain
论文作者
论文摘要
矩阵分解技术一直是分析人脑功能连通性层次结构的重要计算方法。但是，这些方法仍然存在四个缺点：1）。大型培训样品； 2）。手动调整超参数； 3）。耗时，需要广泛的计算源； 4）。它不能保证收敛到唯一的固定点。 因此，我们提出了一种称为深线性基质近似重建（Delmar）的新型深矩阵分解技术，以弥合上述间隙。提出的方法的优点是：起初，提议的德尔玛可以自动估计重要的超参数；此外，德尔玛（Delmar）采用矩阵反向传播来减少潜在的累积错误。最后，引入正交投影以更新Delmar的所有变量，而不是直接计算逆矩阵。 使用人脑的实际功能性MRI信号对三种同行方法和Delmar的验证实验表明，我们所提出的方法可以比其他对等方法有效地更快，更准确地识别fMRI信号中的空间特征。此外，理论分析表明，德尔玛可以收敛到唯一的固定点，甚至可以准确地将原始输入作为DNN。
The Matrix Decomposition techniques have been a vital computational approach to analyzing the hierarchy of functional connectivity in the human brain. However, there are still four shortcomings of these methodologies: 1). Large training samples; 2). Manually tuning hyperparameters; 3). Time-consuming and require extensive computational source; 4). It cannot guarantee convergence to a unique fixed point. Therefore, we propose a novel deep matrix factorization technique called Deep Linear Matrix Approximate Reconstruction (DELMAR) to bridge the abovementioned gaps. The advantages of the proposed method are: at first, proposed DELMAR can estimate the important hyperparameters automatically; furthermore, DELMAR employs the matrix backpropagation to reduce the potential accumulative errors; finally, an orthogonal projection is introduced to update all variables of DELMAR rather than directly calculating the inverse matrices. The validation experiments of three peer methods and DELMAR using real functional MRI signal of the human brain demonstrates that our proposed method can efficiently identify the spatial feature in fMRI signal even faster and more accurately than other peer methods. Moreover, the theoretical analyses indicate that DELMAR can converge to the unique fixed point and even enable the accurate approximation of original input as DNNs.
