Visualizing a multi-dimensional data set in a lower dimensional space
Dong-Hun Seo, Keon Myung Lee · 2008
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.