Maximum margin clustering for state decomposition of metastable systems
Maximum margin clustering for state decomposition of metastable systems
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DOI：
10.1016/j.neucom.2014.12.093
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发表时间：
2013-06
期刊：
影响因子：
6
通讯作者：
Hao Wu
中科院分区：
文献类型：
--
作者：
Hao Wu
在研究亚稳态动力学系统时，一个主要的问题是如何将相空间分解为一组亚稳态。然而，基于模拟或实验数据的亚稳态分解仍然是一个挑战。最流行和最简单的方法是几何聚类，它是在经典聚类技术的基础上发展起来的。然而，这种方法的前提是（1）从处于全局平衡的模拟或实验中获得数据，以及（2）适当地选择坐标系。近年来，基于相空间离散化和转移概率估计的动力学聚类方法因其适用于更一般的情况而受到广泛关注，但离散化策略的选择是一项困难的任务。本文提出了一种新的分解方法--最大间隔亚稳态聚类法，它将亚稳态分解问题转化为一个半监督学习问题，从而可以利用大间隔技术搜索最优分解，而不需要对相空间进行离散化。最后，给出了几个仿真实例，验证了该方法的有效性.
When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states. Unfortunately, the metastable state decomposition based on simulation or experimental data is still a challenge. The most popular and simplest approach is geometric clustering which is developed based on classical clustering techniques. However, the prerequisites of this approach are (1) data are obtained from simulations or experiments which are in global equilibrium and (2) the coordinate system is appropriately selected. Recently, the kinetic clustering approach based on phase space discretization and transition probability estimation has drawn much attention due to its applicability to more general cases, but the choice of discretization policy is a difficult task. In this paper, a new decomposition method designated asmaximum margin metastable clusteringis proposed, which converts the problem of metastable state decomposition to a semi-supervised learning problem so that the large margin technique can be utilized to search for the optimal decomposition without phase space discretization. Moreover, several simulation examples are given to illustrate the effectiveness of the proposed method.