标题： 深度网络的巨几何组织 显示英文标题

标题： Bigeometric Organization of Deep Nets

摘要： 本文中，我们构建了一种高维数据集的组织方式，由于存在缺失项且特征子集中有一部分与感兴趣的建模函数无关，这些数据集无法被干净地嵌入到低维表示中。 我们的算法首先定义点的粗略邻域，并在这些邻域上定义预期的经验函数值。然后，我们生成新的非线性特征，这些特征通过深度网络表示来逼近目标函数，并根据新的表示重新组织点的几何结构。最后，对点进行局部z标准化处理，以创建一种独立于深度网络参数的内在几何组织，这种几何结构旨在确保相对于经验函数的平滑性。 我们在美国医疗保险和医疗补助服务中心医院质量倡议的数据上检验了这种方法，并生成了医院的一种内在低维组织，该组织对于专家驱动的质量函数而言是平滑的。 显示英文摘要

摘要： In this paper, we build an organization of high-dimensional datasets that cannot be cleanly embedded into a low-dimensional representation due to missing entries and a subset of the features being irrelevant to modeling functions of interest. Our algorithm begins by defining coarse neighborhoods of the points and defining an expected empirical function value on these neighborhoods. We then generate new non-linear features with deep net representations tuned to model the approximate function, and re-organize the geometry of the points with respect to the new representation. Finally, the points are locally z-scored to create an intrinsic geometric organization which is independent of the parameters of the deep net, a geometry designed to assure smoothness with respect to the empirical function. We examine this approach on data from the Center for Medicare and Medicaid Services Hospital Quality Initiative, and generate an intrinsic low-dimensional organization of the hospitals that is smooth with respect to an expert driven function of quality.