Generalized incremental manifold learning algorithm based on local smoothness

Abstract

Most of the existing manifold learning algorithms are not capable of dealing with new arrival samples.Although some incremental algorithms are developed via extending a specified manifold learning algorithm,most of them have some disadvantages more or less.In this paper,a new and more Generalized Incremental Manifold Learning(GIML) algorithm based on local smoothness was proposed.GIML algorithm firstly extracted the local smooth structure of data set via local Principal Component Analysis(PCA).Then the optimal linear transformation,which transformed the local smooth structure of new arrival sample's neighborhood to its correspondent low-dimensional embedding coordinates,was computed.Finally the low-dimensional embedding coordinates of new arrival samples were obtained by the optimal transformation.Extensive and systematic experiments were conducted on both artificial and real image data sets.The experimental results demonstrate that the GIML algorithm is an effective incremental manifold learning algorithm and outperforms other existing algorithms.