부산대학교 도서관

The purpose of this study is to develop a method for scalable manifold modeling. A popular method using Gaussian process requires a computational cost of the cubic order for data size, which does not afford to apply large-scale datasets. We aim to achieve the linear order in computational cost using unsupervised kernel regression and its sparse approximation instead of using the Gaussian process regression. For this purpose, we introduce two types of sparse approximations; one is the discretization of the latent space by a grid of inducing points, and the other is a sparse matrix decomposition of the local and global kernel matrices. We evaluated the computation time of the proposed method by using an artificial dataset, and the results showed that the proposed method achieved the linear order in computation time.