We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example ...
Generative Topographic Mapping (GTM) is an important technique for dimension reduction which has been successfully applied to many fields. However the usual Expectation-Maximizat...
Jong Youl Choi, Judy Qiu, Marlon E. Pierce, Geoffr...
Abstract. The generative topographic mapping (GTM) has been proposed as a statistical model to represent high dimensional data by means of a sparse lattice of points in latent spac...
Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for correct clustering. Among several existing measures of topology violation, this paper studies t...