Sketching Datasets for Large-Scale Learning（大规模学习的数据集草图）
Sketching Datasets for Large-Scale Learning (long version)
摘要：
本文考虑“压缩学习”，这是一种大规模机器学习的方法，其中数据集在学习之前被大规模压缩（例如，聚类、分类或回归）。特别地，首先通过计算精心选择的非线性随机特征（例如，随机傅立叶特征）并在整个数据集上对它们进行平均。然后从草图中学习参数，而无需访问原始数据集。本文综述了压缩学习的最新进展，包括主要概念和算法，它们与已建立的信号处理方法的联系，现有的理论保证-信息保护和隐私保护，以及重要的开放问题。
英文摘要：
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification, or regression) is performed. In particular, a "sketch" is first constructed by computing carefully chosen nonlinear random features (e.g., random Fourier features) and averaging them over the whole dataset. Parameters are then learned from the sketch, without access to the original dataset. This article surveys the current state-of-the-art in compressive learning, including the main concepts and algorithms, their connections with established signal-processing methods, existing theoretical guarantees -- on both information preservation and privacy preservation, and important open problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
