论文标题
对增量和减少数据修改的灵敏度分析的更严格的界限估计
Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification
论文作者
论文摘要
在大规模分类问题中，当数据集中添加到或从原始数据集中添加或删除时，数据集始终面临频繁的更新。在这种情况下，传统的增量学习通过明确建模数据修改来更新现有的分类器，比从头开始重新验证新分类器更有效。但是，有时，我们更感兴趣地确定是否应该更新分类器或执行一些敏感性分析任务。为了处理这些此类任务，我们提出了一种算法，以对更新的线性分类器进行合理推断，而无需精确更新分类器。具体而言，所提出的算法可用于估计更新的分类器系数矩阵的上限和下限，其计算复杂性与更新的数据集的大小相关。理论分析和实验结果均表明，就系数的界限和计算复杂性的紧密度而言，所提出的方法优于现有方法。
In large-scale classification problems, the data set always be faced with frequent updates when a part of the data is added to or removed from the original data set. In this case, conventional incremental learning, which updates an existing classifier by explicitly modeling the data modification, is more efficient than retraining a new classifier from scratch. However, sometimes, we are more interested in determining whether we should update the classifier or performing some sensitivity analysis tasks. To deal with these such tasks, we propose an algorithm to make rational inferences about the updated linear classifier without exactly updating the classifier. Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset. Both theoretical analysis and experiment results show that the proposed approach is superior to existing methods in terms of tightness of coefficients' bounds and computational complexity.
