A Constrained Matrix-Variate Gaussian Process for Transposable Data

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Provided by: University of Teramo
Topic: Big Data
Format: PDF
Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values. Additional side information may consist of feature vectors specific to entities corresponding to the rows and/or columns of such a matrix. Further information may also be available in the form of interactions or hierarchies among entities along the same mode (axis). The authors propose a novel approach for modeling transposable data with missing interactions given additional side information. The interactions are modeled as noisy observations from a latent noise free matrix generated from a matrix-variate Gaussian process. The construction of row and column co-variances using side information provides a flexible mechanism for specifying a-prior knowledge of the row and column correlations in the data.
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