函数标记最优划分
Functional Labeled Optimal Partitioning
摘要：
峰值检测是序列数据分析中的一个问题，涉及区分具有较高计数（峰值）的区域和具有较低计数（背景噪声）的区域。在标签的训练和测试集中，正确预测偏离背景噪声的区域是至关重要的。动态规划变点算法已被提出来解决峰值检测问题，通过约束的平均值交替增加，然后减少。目前的约束变点算法只在测试集上创建预测，而完全忽略了训练集。已经提出了在拟合训练集时准确并且在测试集上进行预测的变点算法，但不是在峰值检测模型的上下文中。我们建议解决这些问题，通过创建一个新的动态规划算法，FLOPART，具有零训练标签错误，并能够提供高度准确的预测测试集。我们提供了一个实证分析表明，FLOPART具有类似的时间复杂度，而更准确的训练和测试标签错误方面比现有的算法。
英文摘要：
Peak detection is a problem in sequential data analysis that involves differentiating regions with higher counts (peaks) from regions with lower counts (background noise). It is crucial to correctly predict areas that deviate from the background noise, in both the train and test sets of labels. Dynamic programming changepoint algorithms have been proposed to solve the peak detection problem by constraining the mean to alternatively increase and then decrease. The current constrained changepoint algorithms only create predictions on the test set, while completely ignoring the train set. Changepoint algorithms that are both accurate when fitting the train set, and make predictions on the test set, have been proposed but not in the context of peak detection models. We propose to resolve these issues by creating a new dynamic programming algorithm, FLOPART, that has zero train label errors, and is able to provide highly accurate predictions on the test set. We provide an empirical analysis that shows FLOPART has a similar time complexity while being more accurate than the existing algorithms in terms of train and test label errors.
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。
