An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.
An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.
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DOI：
10.1109/tbdata.2017.2653815
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发表时间：
2018-06
影响因子：
7.2
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Fang H;Zhang Z
大纵向数据为决策提供了更可靠的信息，普遍存在于各个领域。弹道模式识别迫切需要发现此类数据的重要结构。开发更好、计算效率更高的可视化工具是指导这一技术的关键。提出了一种改进的投影寻踪(EPP)方法，以更好地在低维平面上投影和可视化大数据量高维纵向数据的结构(如簇)。与可能对纵向数据有用的经典PP方法不同，EPP建立在非线性映射算法的基础上，通过平衡结构应力之间和结构内部的配对权重来计算其应力(误差)函数，同时保留高维空间中的原始结构成员资格。具体地说，EPP通过集成逐步优化和非线性映射算法来解决NP困难优化问题，并自动搜索最优迭代次数，以显示不同样本大小和维度的稳定结构。使用公开的UCI和真实的纵向临床试验数据集以及模拟，EPP在可视化大的HD纵向数据方面表现出了更好的性能。
Big longitudinal data provide more reliable information for decision making and are common in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important structures for such data. Developing better and more computationally-efficient visualization tool is crucial to guide this technique. This paper proposes an enhanced projection pursuit (EPP) method to better project and visualize the structures (e.g. clusters) of big high-dimensional (HD) longitudinal data on a lower-dimensional plane. Unlike classic PP methods potentially useful for longitudinal data, EPP is built upon nonlinear mapping algorithms to compute its stress (error) function by balancing the paired weights for between and within structure stress while preserving original structure membership in the high-dimensional space. Specifically, EPP solves an NP hard optimization problem by integrating gradual optimization and non-linear mapping algorithms, and automates the searching of an optimal number of iterations to display a stable structure for varying sample sizes and dimensions. Using publicized UCI and real longitudinal clinical trial datasets as well as simulation, EPP demonstrates its better performance in visualizing big HD longitudinal data.