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This study deals with missing link prediction, the problem of predicting the existence of missing connections between entities of interest. We approach the problem as filling in missing entries in a relational dataset represented by several matrices and multiway arrays, that will be simply called tensors. Consequently, we address the link prediction problem(More)
This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach(More)
Binary matrices and tensors are popular data structures that need to be efficiently approximated by low-rank representations. A standard approach is to minimize the logistic loss, well suited for binary data. In many cases, the number m of non-zero elements in the tensor is much smaller than the total number n of possible entries in the tensor. This creates(More)
Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints. Recently, varia-tional Bayesian (VB) inference techniques have successfully been applied to large scale models. This paper presents full Bayesian inference via VB on both single and coupled tensor factorization(More)
Incorporating domain-specific side information via coupled factorization models is useful in source separation applications. Coupled models can easily incorporate information from source modalities with different statistical properties by estimating shared factors via divergence minimization. Here, it is useful to use mixed divergences, a specific(More)
This study deals with the missing answers prediction problem. We address this problem using coupled analysis of ImageCLEF2014 dataset by representing it as a heterogeneous data, i.e., dataset in the form of matrices. We propose to use an approach based on probabilistic interpretation of tensor factorization models, i.e., Generalized Coupled Tensor(More)
Coupled tensor factorization methods are useful for sensor fusion, combining information from several related datasets by simultaneously approximating them by products of latent tensors. In these methods, the choice of a suitable optimization criteria becomes difficult as observed datasets may have different statistical characteristics and their relative(More)
—Probabilistic Latent Tensor Factorization (PLTF) is a recently proposed probabilistic framework for modelling multi-way data. Not only the common tensor factorization models but also any arbitrary tensor factorization structure can be realized by the PLTF framework. This paper presents full Bayesian inference via variational Bayes that facilitates more(More)
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