Matrix Visualization: New Generation of Exploratory Data Analysis
报告人： Chun-houh Chen, Institute of Statistical Science, Academia Sinica
时间：2016-10-20 14:00 ~ 15:00
“It is important to understand what you CAN DO before you learn to measure how WELL you seem to have DONE it” (Exploratory Data Analysis: John Tukey, 1977). Data analysts and statistics practitioners nowadays are facing difficulties in understanding higher and higher dimensional data with more and more complex nature while conventional graphics/visualization tools do not answer the needs. It is statisticians’ responsibility for coming up with graphics/visualization environment that can help users really understand what one CAN DO for complex data generated from modern techniques and sophisticated experiments. Matrix visualization (MV) for continuous, binary, ordinal, and nominal data with various types of extensions provide users more comprehensive information embedded in complex high dimensional data than conventional EDA tools such as boxplot, scatterplot, with dimension reduction techniques such as principal component analysis and multiple correspondence analysis. In this talk I’ll summarize our works on creating MV environment for conducting statistical analyses and introducing statistical concepts into MV environment for visualizing more versatile and complex data structure. Many real world examples will be demonstrated in this talk for illustrating the strength of MV for visualizing all types of datasets collected from scientific experiments and social surveys.
About the Speaker:
Chun-houh Chen is Research Fellow and Deputy Director at the Institute of Statistical Science, Academia Sinica, and President of the Chinese Institute of Probability and Statistics. He received his Ph.D. from University of California, Los Angeles in 1992. His research interests include bioinformatics, data/information visualization, dimension reduction, matrix visualization, multivariate statistical methods, and pattern recognition. He is an elected member of the International Statistical Institute.