摘要： 将平行坐标用于高维数据的可视化时，如果要展示的数据维太多，会发生可视化混乱。针对上述问题，提出一种结合主成分分析(PCA)和平行坐标的数据可视化方法PPCP。利用PCA方法对高维数据进行有效的降维处理，将降维后的数据进行平行坐标可视化展示。实验结果证明，该方法能有效地揭示高维数据之间的关系。
关键词:
主成分分析,
平行坐标,
可视化,
高维数据
Abstract: Parallel coordinates can be used in high-dimensional data visualization, but when the data dimension to be displayed is too large, visual clutter may occur. This paper proposes a data visualization method named PPCP, which combines Principal Component Analysis(PCA) and parallel coordinate. PCA is used for effective dimension reduction on high-dimensional data, and the processed data are displayed in the way of parallel coordinate visualization. Experimental results show that it is effective to reveal the relationships among high-dimensional data.
Key words:
Principal Component Analysis(PCA),
parallel coordinate,
visualization,
high-dimensional data
中图分类号:
雷君虎, 杨家红, 钟坚成, 王苏卫. 基于PCA和平行坐标的高维数据可视化[J]. 计算机工程, 2011, 37(01): 48-50.
LEI Jun-Hu, YANG Jia-Gong, ZHONG Jian-Cheng, WANG Su-Wei. High-dimensional Data Visualization Based on Principal Component Analysis and Parallel Coordinate[J]. Computer Engineering, 2011, 37(01): 48-50.