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An Efficient Multidimensional Scaling Method based on CUDA and Divide-and-Conquer  

Park, Sung-In (숭실대학교 컴퓨터학과)
Hwang, Kyu-Baek (숭실대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.4, 2010 , pp. 427-431 More about this Journal
Abstract
Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.
Keywords
multidimensional scaling; GPGPU; CUDA; unsupervised learning; divide-and-conquer;
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