论文标题
使用无调的方法稳健的凸倍数
Robust convex biclustering with a tuning-free method
论文作者
论文摘要
双簇通过有效地发现样品与特征之间的局部相关性，广泛用于不同类型的领域，包括基因信息分析，文本挖掘和推荐系统。但是，在面对重尾数据时，许多双簇算法将崩溃。在本文中，我们提出了具有Huber损失的强大版本的凸双散发算法。但是，新引入的鲁棒化参数为选择最佳参数带来了额外的负担。因此，我们提出了一种无调的方法，用于自动以高效率选择最佳鲁棒化参数。仿真研究表明，在遇到重尾噪声时，我们提出的方法的性能比传统的双簇方法更为出色。还提出了现实生活中的生物医学应用。 R软件包rcvxbiclustr可以在https://github.com/yifanchen3/rcvxbiclustrust上获得。
Biclustering is widely used in different kinds of fields including gene information analysis, text mining, and recommendation system by effectively discovering the local correlation between samples and features. However, many biclustering algorithms will collapse when facing heavy-tailed data. In this paper, we propose a robust version of convex biclustering algorithm with Huber loss. Yet, the newly introduced robustification parameter brings an extra burden to selecting the optimal parameters. Therefore, we propose a tuning-free method for automatically selecting the optimal robustification parameter with high efficiency. The simulation study demonstrates the more fabulous performance of our proposed method than traditional biclustering methods when encountering heavy-tailed noise. A real-life biomedical application is also presented. The R package RcvxBiclustr is available at https://github.com/YifanChen3/RcvxBiclustr.
