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适应性稀疏高斯过程

Vanessa Gomez-Verdejo, Emilio Parrado-Hernandez, Manel Martinez-Ramon

IEEE transactions on neural networks and learning systems

|July 19, 2023

概括

本研究介绍了适应性稀疏高斯过程 (GP) 对于非静止环境. 这种新的方法有效地更新了具有遗忘因子的模型,使机器智能的快速,低成本的在线学习成为可能.

科学领域:

机器学习机器学习
人工智能的人工智能
统计建模统计建模

背景情况:

适应性学习对于非静止环境至关重要,它要求机器忘记过时的数据分布.
高效的算法需要紧的,计算上便宜的模型更新来进行在线参数调整.
目前的解决方案不足以应对这些适应性学习挑战.

研究的目的:

提出第一个适应性稀疏高斯过程 (GP),解决计算效率和非静止性问题.
开发一种以最小的计算成本进行紧模型更新的方法.
在动态环境中实现有效的在线参数更新.

主要方法:

重构了一个变量稀疏GP (VSGP) 算法,其中包含了适应性的忘记因子.
开发了一种新方法,只更新一个单一的诱导点和每个新数据样本的模型参数.
专注于简化模型推理以实现高效的在线处理.

主要成果:

拟议的算法在推断过程中显示出快速的趋同.
通过单个推理代实现了高效的模型更新,即使在高度非静止的设置中也是如此.
在预测后平均值和置信区间估计方面表现强.

结论:

适应性稀疏GP通过忘记过去的数据,有效地处理非静止环境.
该方法通过紧的,单次代模型更新提供计算效率.
在建模预测后期和信心区间方面表现优于最先进的方法.

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