发表机构
National Graduate Institute for Policy Studies (GRIPS); Riken AIP（政策研究大学院大学（GRIPS）; 理化学研究所人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合经验局部化与变形Bregman散度的方法，用于离散非归一化模型的参数估计，以降低计算成本并获得高效、鲁棒的估计器。
AI 中文摘要
概率模型的参数估计是机器学习领域的一项重要任务。对于离散变量模型，模型归一化常数的计算有时很困难，许多研究致力于避免计算归一化常数。本文通过结合经验局部化技术和变形Bregman散度来应对这一难题。经验局部化技术能够大幅降低归一化常数计算的计算成本，此外，适当选择Bregman散度的变形形式可以为所提出的估计器赋予各种有利的统计性质，如高效性或对异常噪声的鲁棒性。
英文摘要
Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of the normalization constant of model is sometimes difficult and a lot of researches have been done to avoid the calculation of the normalization constant. In this paper, we tackle with the difficulty by combining a technique of empirical localization and a deformed Bregman divergence.The technique of empirical localization makes it possible to drastically reduce computational cost of the calculation of the normalization constant, and in addition, appropriate choice of the deformation for the Bregman divergence can invest the proposed estimator with various kinds of favorable statistical properties, such as efficiency or robustness against outlier noise.
Comments29 pages, 9 figures
