Penalty learning for changepoint detection
Penalty learning for changepoint detection
复制标题
变化点检测的惩罚学习
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
N. Vayatis
中科院分区：
文献类型：
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作者：
Charles Truong;L. Oudre;N. Vayatis
我们考虑在监督学习的设置中的信号分割问题。这里的监督在于在类似信号的历史数据库中存在标记的变化点。典型的分割技术依赖于惩罚最小二乘过程，其中平滑参数是任意固定的。我们引入alpin（自适应线性惩罚推理）算法来自动调整平滑参数。ALPIN具有线性复杂度相对于样本大小，并证明是强大的噪声和不同的注释策略。数值实验表明ALPIN的效率相比，国家的最先进的方法。
We consider the problem of signal segmentation in the setup of supervised learning. The supervision lies here in the existence of labelled change points in a historical database of similar signals. Typical segmentation techniques rely on a penalized least square procedure where the smoothing parameter is fixed arbitrarily. We introduce the alpin (Adaptive Linear Penalty INference) algorithm to tune automatically the smoothing parameter. ALPIN has linear complexity with respect to the sample size and turns out to be robust with respect to noise and diverse annotation strategies. Numerical experiments reveal the efficiency of ALPIN compared to state-of-the-art methods.