Title :
Visualizing a multi-dimensional data set in a lower dimensional space
Author :
Seo, Dong-Hun ; Lee, Won Don
Author_Institution :
Dept. of Comput. Sci. & Eng., ChungNam Nat. Univ., Daejeon
Abstract :
This paper presents a method of visualizing a multi-dimensional data set into a lower dimensional space, especially into a two-dimensional space, so that people can intuitively conceive the relations or the distance between the entities of the data. Kullback-Leibler divergence is used as the measure to evaluate the distance between the vectors of the probability distribution. The measured distance values are used to find the corresponding coordinates of the entities in a lower dimensional space. Here, the one variable stochastic simulated annealing (OVSSA) is employed as the optimization technique. Experiments show that this is a plausible way of visualizing the multi-dimensional data, letting people see the relations among the entities intuitively.
Keywords :
data visualisation; simulated annealing; statistical distributions; Kullback-Leibler divergence; lower dimensional space; measured distance value; multidimensional data set visualization; one variable stochastic simulated annealing; optimization technique; probability distribution; Arithmetic; Chromium; Computer science; Covariance matrix; Data engineering; Data visualization; Eigenvalues and eigenfunctions; Principal component analysis; Probability distribution; Simulated annealing; Dimension Reduction; Visualization;
Conference_Titel :
Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008. First International Conference on the
Conference_Location :
Ostrava
Print_ISBN :
978-1-4244-2623-2
Electronic_ISBN :
978-1-4244-2624-9
DOI :
10.1109/ICADIWT.2008.4664363