The architectures of deep neural networks (DNN) rely heavily on the underlying grid structure of variables, for instance, the lattice of pixels in an image. For general high dimensional data with variables not associated with a grid, the multi-layer perceptron and deep belief network are often used. However, it is frequently observed that those networks do not perform competitively and they are not helpful for identifying important variables. In this paper, we propose a framework that imposes on blocks of variables a chain structure obtained by step-wise greedy search so that the DNN architecture can leverage the constructed grid. We call this new neural network Deep Variable-Block Chain (DVC). Because the variable blocks are used for classification in a sequential manner, we further develop the capacity of selecting variables adaptively according to a number of regions trained by a decision tree. Our experiments show that DVC outperforms other generic DNNs and other strong classifiers. Moreover, DVC can achieve high accuracy at much reduced dimensionality and sometimes reveals drastically different sets of relevant variables for different regions.

翻译：深度神经网络（DNN）的架构在很大程度上依赖于变量的底层网格结构，例如图像中像素的晶格。对于变量不与网格关联的一般高维数据，通常使用多层感知机和深度信念网络。然而，经常观察到这些网络的性能缺乏竞争力，并且它们无助于识别重要变量。在本文中，我们提出了一种框架，该框架通过逐步贪婪搜索获得的链结构对变量块施加约束，从而使DNN架构能够利用所构建的网格。我们将这种新的神经网络称为深度变量块链（DVC）。由于变量块以顺序方式用于分类，我们进一步开发了根据决策树训练的多个区域自适应选择变量的能力。我们的实验表明，DVC优于其他通用DNN和其他强分类器。此外，DVC能够在维度大幅降低的情况下实现高精度，并且有时会为不同区域揭示截然不同的相关变量集。