Tensors or multiway arrays are functions of three or more indices (i, j, k,...)-similar to matrices
(two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

The topic of semantic segmentation has witnessed considerable progress due to the
powerful features learned by convolutional neural networks (CNNs). The current leading …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

The success of machine learning–based studies is largely subjected to accessing a large
amount of data. However, accessing such data is typically not feasible within a single health …

The CANDECOMP/PARAFAC (CP) decomposition is a leading method for the analysis of
multiway data. The standard alternating least squares algorithm for the CP decomposition …

Social networks are becoming larger and more complex as new ways of collecting social
interaction data arise (namely from online social networks, mobile devices sensors,...) …

Computational phenoty** is the process of converting heterogeneous electronic health
records (EHRs) into meaningful clinical concepts. Unsupervised phenoty** methods have …

How can we find useful patterns and anomalies in large scale real-world data with multiple
attributes? For example, network intrusion logs, with (source-ip, target-ip, port-number …

With a goal of identifying biomarkers/patterns related to certain conditions or diseases,
metabolomics focuses on the detection of chemical substances in biological samples such …