Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions
Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …
multidimensional data of exceedingly high volume, variety, and structural richness …
Tensor ring decomposition
Tensor networks have in recent years emerged as the powerful tools for solving the large-
scale optimization problems. One of the most popular tensor network is tensor train (TT) …
scale optimization problems. One of the most popular tensor network is tensor train (TT) …
Tensor decompositions for signal processing applications: From two-way to multiway component analysis
The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …
highlighted the limitations of standard flat-view matrix models and the necessity to move …
Bayesian CP factorization of incomplete tensors with automatic rank determination
CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful
technique for tensor completion through explicitly capturing the multilinear latent factors. The …
technique for tensor completion through explicitly capturing the multilinear latent factors. The …
Low-rank tensor networks for dimensionality reduction and large-scale optimization problems: Perspectives and challenges part 1
Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …
analyzing large volume, multi-relational and multi--modal datasets, which are often …
Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization
The rapidly increasing availability of electronic health records (EHRs) from multiple
heterogeneous sources has spearheaded the adoption of data-driven approaches for …
heterogeneous sources has spearheaded the adoption of data-driven approaches for …
Bayesian models of graphs, arrays and other exchangeable random structures
The natural habitat of most Bayesian methods is data represented by exchangeable
sequences of observations, for which de Finetti's theorem provides the theoretical …
sequences of observations, for which de Finetti's theorem provides the theoretical …
Bayesian robust tensor factorization for incomplete multiway data
We propose a generative model for robust tensor factorization in the presence of both
missing data and outliers. The objective is to explicitly infer the underlying low …
missing data and outliers. The objective is to explicitly infer the underlying low …
Learning compact recurrent neural networks with block-term tensor decomposition
Abstract Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs becomes …
However, when dealing with high dimensional inputs, the training of RNNs becomes …
Linked component analysis from matrices to high-order tensors: Applications to biomedical data
With the increasing availability of various sensor technologies, we now have access to large
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …