Active Learning with Maximum Margin Sparse Gaussian Processes
Active Learning with Maximum Margin Sparse Gaussian Processes
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发表时间：
2021
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通讯作者：
Weishi Shi;Qi Yu
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作者：
Weishi Shi;Qi Yu
本文提出了一种用于多类问题分类模型主动学习的最大间隔稀疏高斯过程（MM-SGP）。该模型通过将最大边际约束集成到GP的学习过程中，对GP进行了新的扩展，旨在进一步提高其预测能力，同时保持其固有的不确定性量化能力。MM约束确保了模型的小“有效大小”，这允许MM-SGP通过使用有限的“活动”数据样本来提供良好的预测性能，这是AL的关键属性。此外，作为高斯过程模型，MM-SGP将输出预测的类别分布和预测方差，这两者对于定义有效的采样函数以同时改善大量类的决策边界都是必不可少的。最后，MM-SGP的稀疏性确保了它可以通过解决低秩凸对偶问题来有效地训练。在人工数据集和真实数据集上的实验结果表明了该模型的有效性和高效性。
We present a maximum-margin sparse Gaussian Process (MM-SGP) for active learning (AL) of classification models for multi-class problems. The proposed model makes novel extensions to a GP by integrating maximum-margin constraints into its learning process, aiming to further improve its predictive power while keeping its inherent capability for uncertainty quantification. The MM constraints ensure small “effective size” of the model, which allows MM-SGP to provide good predictive performance by using limited “active” data samples, a critical property for AL. Furthermore, as a Gaussian process model, MM-SGP will output both the predicted class distribution and the predictive variance, both of which are essential for defining a sampling function effective to improve the decision boundaries of a large number of classes simultaneously. Finally, the sparse nature of MM-SGP ensures that it can be efficiently trained by solving a low-rank convex dual problem. Experiment results on both synthetic and real-world datasets show the effectiveness and efficiency of the proposed AL model.