PROJECT TITLE :

Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

ABSTRACT:

The widespread use of multisensor technology and the emergence of big data sets have highlighted the restrictions of normal flat-view matrix models and the necessity to move toward a lot of versatile knowledge analysis tools. We tend to show that higher-order tensors (i.e., multiway arrays) enable such a basic paradigm shift toward models that are essentially polynomial, the individuality of which, not like the matrix strategies, is guaranteed underneath terribly mild and natural conditions. Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to possess great flexibility in the selection of constraints that match knowledge properties and extract additional general latent components in the info than matrix-primarily based ways.