Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition

Extracting Phenotypes from Patient Claim Records Using Nonnegative Tensor Factorization

Multi-document Summarization using Tensor Decomposition

The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as poss...

Blind multispectral image decomposition by 3D nonnegative tensor factorization.

Alpha-divergence-based nonnegative tensor factorization (NTF) is applied to blind multispectral image (MSI) decomposition. The matrix of spectral profiles and the matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models. NTF preserves local structure in the MSI that is lost as a result of vectorization of the image when...

Multi-relational Weighted Tensor Decomposition

In real-world network data, there often exist multiple types of relationships (edges) that we would like to model. For instance, in social networks, relationships between individuals may be personal, familial, or professional. In this paper, we examine a multi-relational learning scenario in which the learner is given a small set of training examples, sampled from the complete set of potential ...

Higher Order Dynamic Mode Decomposition