Relational generative topographic mapping

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 space, such that visualization, compression, and data inspection become possible. Original GTM is restricted to Euclidean data points in a vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.

• Type: Journal
• Year: 2011
• Where: IJON
• Authors: Andrej Gisbrecht, Bassam Mokbel, Barbara Hammer
• Keywords: Data Points, IJON 2011, Space, Vector Space