Provided by: Cornell University
Topic: Big Data
Date Added: Mar 2014
Modern applications such as computational neuroscience, neuroinformatics and pattern/image recognition generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Big data require novel technologies to efficiently process massive datasets within tolerable elapsed times. Such a new emerging technology for multidimensional big data is a multi-way analysis via Tensor Networks (TNs) and Tensor Decompositions (TDs) which decompose tensors to sets of factor (component) matrices and low-order (core) tensors. Tensors (i.e., multi-way arrays) provide a natural and compact representation for such massive multidimensional data via suitable low-rank approximations.