Díaz-Pachón Daniel Andrés, Sáenz Juan Pablo, Rao J Sunil, Dazard Jean-Eudes
Division of Biostatistics, Don Soffer Clinical Research Center, University of Miami, Miami, Florida.
Department of Industrial Engineering, University of Miami, Coral Gables, Florida.
Appl Stoch Models Bus Ind. 2019 Mar-Apr;35(2):376-393. doi: 10.1002/asmb.2430. Epub 2019 Jan 31.
We propose a new method to find modes based on active information. We develop an algorithm called active information mode hunting (AIMH) that, when applied to the whole space, will say whether there are any modes present where they are. We show AIMH is consistent and, given that information increases where probability decreases, it helps to overcome issues with the curse of dimensionality. The AIMH also reduces the dimensionality with no resource to principal components. We illustrate the method in three ways: with a theoretical example (showing how it performs better than other mode hunting strategies), a real dataset business application, and a simulation.
我们提出了一种基于有效信息来寻找模态的新方法。我们开发了一种名为有效信息模态搜索（AIMH）的算法，当将其应用于整个空间时，它能判断是否存在任何模态以及它们所在的位置。我们证明了AIMH是一致的，并且鉴于信息在概率降低的地方增加，它有助于克服维度诅咒问题。AIMH在不借助主成分分析资源的情况下也能降低维度。我们通过三种方式对该方法进行了说明：一个理论示例（展示它如何比其他模态搜索策略表现更好）、一个真实数据集的商业应用以及一次模拟。
