Title
An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data
UMMS Affiliation
Department of Quantitative Health Sciences
Publication Date
6-1-2018
Document Type
Article
Disciplines
Computer Sciences | Library and Information Science | Translational Medical Research
Abstract
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.
Keywords
UMCCTS funding, Enhanced projection pursuit, Longitudinal data, Pattern recognition, Visualization
DOI of Published Version
10.1109/TBDATA.2017.2653815
Source
IEEE Trans Big Data. 2018 Jun;4(2):289-298. doi: 10.1109/TBDATA.2017.2653815. Epub 2017 Jan 16. Link to article on publisher's site
Journal/Book/Conference Title
IEEE transactions on big data
Related Resources
PubMed ID
29888298
Repository Citation
Fang, Hua (Julia) and Zhang, Zhaoyang, "An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data" (2018). UMass Center for Clinical and Translational Science Supported Publications. 141.
https://escholarship.umassmed.edu/umccts_pubs/141