In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …

We propose a simple two-step approach for speeding up convolution layers within large
convolutional neural networks based on tensor decomposition and discriminative fine …

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 …

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful
technique for tensor completion through explicitly capturing the multilinear latent factors. The …

Many problems in computational neuroscience, neuroinformatics, pattern/image recognition,
signal processing and machine learning generate massive amounts of multidimensional …

In recent years, low-rank based tensor completion, which is a higher order extension of
matrix completion, has received considerable attention. However, the low-rank assumption …

Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal
processing and recommender systems. Real-world tensors can be enormous in size and …

We propose a general algorithmic framework for constrained matrix and tensor factorization,
which is widely used in signal processing and machine learning. The new framework is a …

We present structured data fusion (SDF) as a framework for the rapid prototyping of
knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set …

Higher-order tensors and their decompositions are abundantly present in domains such as
signal processing (eg, higher-order statistics [1] and sensor array processing [2]), scientific …