Abstract As a typical method for multivariate statistical analysis, multidimensional scaling(MDS) has been widely used in dimensionality reduction and visualization. Based on a distance matrix of n samples, the coordinates of MDS in a low-dimensional Euclidean space are obtained. The time complexity for classical MDS algorithm(CMDS) is Θ(n3), which affects the speed of MDS. A new MDS algorithm based on the idea of divide-and-conquer is proposed. The distance matrix is divided into several submatrices along its diagonal, then the submatrices are solved respectively. With orthogonal transformation and translation transformation, all of these subproblem solutions are aligned to get the global solution to the original distance matrix. The result of the proposed algorithm is completely the same as that of CMDS. When the dimensionality of sample space is far less than the number of samples, the time complexity of the proposed algorithm is only Θ(nlgn). Thus, compared with CMDS, the speed of the proposed algorithm is greatly improved, which makes MDS applicable to larger datasets.
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