CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.10  Oct.
Subscribe
Issue No.10  Oct. (2013 vol.35)
pp: 23402356
Tingting Mu , Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK
J. Y. Goulermas , Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.66
ABSTRACT
In this paper, we study the coembedding problem of how to map different types of patterns into one common lowdimensional space, given only the associations (relation values) between samples. We conduct a generic analysis to discover the commonalities between existing coembedding algorithms and indirectly related approaches and investigate possible factors controlling the shapes and distributions of the coembeddings. The primary contribution of this work is a novel method for computing coembeddings, termed the automatic coembedding with adaptive shaping (ACAS) algorithm, based on an efficient transformation of the coembedding problem. Its advantages include flexible model adaptation to the given data, an economical set of model variables leading to a parametric coembedding formulation, and a robust model fitting criterion for model optimization based on a quantization procedure. The secondary contribution of this work is the introduction of a set of generic schemes for the qualitative analysis and quantitative assessment of the output of coembedding algorithms, using existing labeled benchmark datasets. Experiments with synthetic and realworld datasets show that the proposed algorithm is very competitive compared to existing ones.
INDEX TERMS
Vectors, Computational modeling, Algorithm design and analysis, Eigenvalues and eigenfunctions, Large scale integration, Adaptation models, Data models,structural matching, Relational data, data coembedding, heterogeneous embedding, data visualization
CITATION
Tingting Mu, J. Y. Goulermas, "Automatic Generation of CoEmbeddings from Relational Data with Adaptive Shaping", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 10, pp. 23402356, Oct. 2013, doi:10.1109/TPAMI.2013.66
REFERENCES
