时变模型的改进估计
Improved Estimation in Time Varying Models
摘要：
模型的局部自适应参数化（如局部加权回归）是有表现力的，但通常会受到高方差的影响。我们描述了一种减少方差的方法，基于同时估计模型的变换空间以及在这个新空间中局部适应的参数化的想法。我们提出了一个新的问题制定，捕捉这个想法，并说明它的重要背景下随时间变化的模型。我们开发了一种算法，用于学习一组近似时变稀疏网络的基础，每个学习的基础构成一个原型稀疏网络结构。我们还为学习任务驱动的基础提供了扩展。我们目前的经验结果合成数据集，以及BCI EEG分类任务。
英文摘要：
Locally adapted parameterizations of a model (such as locally weighted regression) are expressive but often suffer from high variance. We describe an approach for reducing the variance, based on the idea of estimating simultaneously a transformed space for the model, as well as locally adapted parameterizations in this new space. We present a new problem formulation that captures this idea and illustrate it in the important context of time varying models. We develop an algorithm for learning a set of bases for approximating a time varying sparse network; each learned basis constitutes an archetypal sparse network structure. We also provide an extension for learning task-driven bases. We present empirical results on synthetic data sets, as well as on a BCI EEG classification task.
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。
