Passage method for nonlinear dimensionality reduction of data on multi-cluster manifolds
MetadataShow full item record
Nonlinear dimensionality reduction of data lying on multi-cluster manifolds is a crucial issue in manifold learning research. An effective method, called the passage method, is proposed in this paper to alleviate the disconnectivity, short-circuit, and roughness problems ordinarily encountered by the existing methods. The specific characteristic of the proposed method is that it constructs a globally connected neighborhood graph superimposed on the data set through technically building the smooth passages between separate clusters, instead of supplementing some rough inter-cluster connections like some existing methods. The neighborhood graph so constructed is naturally configured as a smooth manifold, and hence complies with the effectiveness condition underlying manifold learning. This theoretical argument is supported by a series of experiments performed on the synthetic and real data sets residing on multi-cluster manifolds. © 2013 Elsevier Ltd.
Showing items related by title, author, creator and subject.
Meng, D.; Leung, Yee-Hong; Xu, Z. (2013)Detecting intrinsic loop structures of a data manifold is the necessary prestep for the proper employment of the manifold learning techniques and of fundamental importance in the discovery of the essential representational ...
Li, Qilin (2016)We proposed two novel clustering approaches, AFS and AFSSC, to address the problems in image clustering, semantic learning and manifold learning, respectively. By applying fuzzy membership function for data representation ...
Chen, X.; Fan, K.; Liu, Wan-Quan; Zhang, X.; Xue, M. (2014)Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional feature space with possible better discriminative structure. In this paper, we propose a supervised manifold learning ...