Bayesian Factorizations of Big Sparse Tensors
نویسندگان
چکیده
Bayesian Factorizations of Big Sparse Tensors Jing Zhou, Anirban Bhattacharya, Amy H. Herring & David B. Dunson To cite this article: Jing Zhou, Anirban Bhattacharya, Amy H. Herring & David B. Dunson (2015) Bayesian Factorizations of Big Sparse Tensors, Journal of the American Statistical Association, 110:512, 1562-1576, DOI: 10.1080/01621459.2014.983233 To link to this article: http://dx.doi.org/10.1080/01621459.2014.983233
منابع مشابه
Scalable Boolean Tensor Factorizations using Random Walks
Tensors are becoming increasingly common in data mining, and consequently, tensor factorizations are becoming more and more important tools for data miners. When the data is binary, it is natural to ask if we can factorize it into binary factors while simultaneously making sure that the reconstructed tensor is still binary. Such factorizations, called Boolean tensor factorizations, can provide ...
متن کاملNewton-Based Optimization for Nonnegative Tensor Factorizations
Tensor factorizations with nonnegative constraints have found application in analyzing data from cyber traffic, social networks, and other areas. We consider application data best described as being generated by a Poisson process (e.g., count data), which leads to sparse tensors that can be modeled by sparse factor matrices. In this paper we investigate efficient techniques for computing an app...
متن کاملRegularized Tensor Factorizations and Higher-Order Principal Components Analysis
High-dimensional tensors or multi-way data are becoming prevalent in areas such as biomedical imaging, chemometrics, networking and bibliometrics. Traditional approaches to finding lower dimensional representations of tensor data include flattening the data and applying matrix factorizations such as principal components analysis (PCA) or employing tensor decompositions such as the CANDECOMP / P...
متن کاملOn Tensors, Sparsity, and Nonnegative Factorizations
Tensors have found application in a variety of fields, ranging from chemometrics to signal processing and beyond. In this paper, we consider the problem of multilinear modeling of sparse count data. Our goal is to develop a descriptive tensor factorization model of such data, along with appropriate algorithms and theory. To do so, we propose that the random variation is best described via a Poi...
متن کاملNewton-based optimization for Kullback-Leibler nonnegative tensor factorizations
Tensor factorizations with nonnegative constraints have found application in analyzing data from cyber traffic, social networks, and other areas. We consider application data best described as being generated by a Poisson process (e.g., count data), which leads to sparse tensors that can be modeled by sparse factor matrices. In this paper we investigate efficient techniques for computing an app...
متن کامل