A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats
A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats
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一种新颖的功能数据聚类正则化方法：在奶山羊挤奶动力学中的应用
DOI：
10.1111/rssc.12404
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
L. Sansonnet
中科院分区：
文献类型：
--
作者：
Christophe Denis;Christophe Denis;Christophe Denis;É. Lebarbier;É. Lebarbier;C. Lévy;C. Lévy;Olivier Martin;Olivier Martin;L. Sansonnet;L. Sansonnet
出于应用的奶山羊挤奶动力学的聚类，我们提出了一种新的方法功能数据聚类。这一问题在精确畜牧业中引起了越来越多的兴趣，这在很大程度上是基于数据采集自动化的发展和解释工具的发展，以利用高通量原始数据并生成表型性状的基准。我们在论文中提出的方法福尔斯这种情况。我们的方法依赖于基于一种新的正则化变点估计方法的曲线的分段线性估计，以及应用于总结曲线的系数向量的k均值算法。我们的方法的统计性能进行评估，通过数值实验，并与现有的实验进行了彻底的比较。我们的技术最终应用于牛奶排放动力学数据，目的是更好地表征动物间的变异性，并更好地了解泌乳过程。
Motivated by an application to the clustering of milking kinetics of dairy goats, we propose a novel approach for functional data clustering. This issue is of growing interest in precision livestock farming, which is largely based on the development of data acquisition automation and on the development of interpretative tools to capitalize on high throughput raw data and to generate benchmarks for phenotypic traits. The method that we propose in the paper falls in this context. Our methodology relies on a piecewise linear estimation of curves based on a novel regularized change‐point‐estimation method and on the k‐means algorithm applied to a vector of coefficients summarizing the curves. The statistical performance of our method is assessed through numerical experiments and is thoroughly compared with existing experiments. Our technique is finally applied to milk emission kinetics data with the aim of a better characterization of interanimal variability and towards a better understanding of the lactation process.
影响因子：
3.5
作者：
AUGER, IE;LAWRENCE, CE
通讯作者：
LAWRENCE, CE