Computation of heterogeneous object co-embeddings from relational measurements. (May 2017)

• Record Type: Journal Article
• Authors: Wu, Yu; Mu, Tingting; Liatsis, Panos; Goulermas, John Y.
• Is Part Of: Pattern recognition. Volume 65(2017:May)
• Page Start: 146
• Page End: 163
• Publication Date: 2017-05
• Subjects: Co-embedding generation -- Relational information -- Heterogeneous object analysis -- Joint space projection.; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.12.004
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Dimensionality reduction and data embedding methods generate low dimensional representations of a single type of homogeneous data objects. In this work, we examine the problem of generating co-embeddings or pattern representations from two different types of objects within a joint common space of controlled dimensionality, where the only available information is assumed to be a set of pairwise relations or similarities between instances of the two groups. We propose a new method that models the embedding of each object type symmetrically to the other type, subject to flexible scale constraints and weighting parameters. The embedding generation relies on an efficient optimization dispatched using matrix decomposition, that is also extended to support multidimensional co-embeddings. We also propose a scheme of heuristically reducing the parameters of the model, and a simple way of measuring the conformity between the original object relations and the ones re-estimated from the co-embeddings, in order to achieve model selection by identifying the optimal model parameters with a simple search procedure. The capabilities of the proposed method are demonstrated with multiple synthetic and real-world datasets from the text mining domain. The experimental results and comparative analyses indicate that the proposed algorithm outperforms existing methods for co-embedding generation. Highlights: Analysis of arbitrary relational information between heterogeneous objects. Explicit generation of co-embeddings directly from relational measurements. High efficiency due to simple matrix decomposition and a small set of parameters. Simple scheme to implement the model identification of the parameters. Multiple comparisons with synthetic and real-world datasets, and existing algorithms.