论文标题
统计学习保证压缩聚类和压缩混合物建模
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
论文作者
论文摘要
在压缩统计学习的背景下，我们为两项无监督的学习任务提供了统计学习保证，这是我们在伴侣论文中介绍的资源有效大规模学习的一般框架。压缩统计学习的原理是压缩培训收集的原则，一方面是一个通行证，将其用于低维的草图（随机的巨型总体上），将其捕获为一项研究，以捕获有关信息的信息。我们明确描述和分析随机特征函数，这些功能将经验平均值保留所需的信息，用于具有固定已知方差的压缩聚类和压缩高斯混合物建模，并在问题维度上建立了足够的草图大小。
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper.The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task. We explicitly describe and analyze random feature functions which empirical averages preserve the needed information for compressive clustering and compressive Gaussian mixture modeling with fixed known variance, and establish sufficient sketch sizes given the problem dimensions.
