1 research outputs found
Tensor Low Rank Modeling and Its Applications in Signal Processing
Modeling of multidimensional signal using tensor is more convincing than
representing it as a collection of matrices. The tensor based approaches can
explore the abundant spatial and temporal structures of the mutlidimensional
signal. The backbone of this modeling is the mathematical foundations of tensor
algebra. The linear transform based tensor algebra furnishes low complex and
high performance algebraic structures suitable for the introspection of the
multidimensional signal. A comprehensive introduction of the linear transform
based tensor algebra is provided from the signal processing viewpoint. The rank
of a multidimensional signal is a precious property which gives an insight into
the structural aspects of it. All natural multidimensional signals can be
approximated to a low rank signal without losing significant information. The
low rank approximation is beneficial in many signal processing applications
such as denoising, missing sample estimation, resolution enhancement,
classification, background estimation, object detection, deweathering,
clustering and much more applications. Detailed case study of the ways and
means of the low rank modeling in the above said signal processing applications
are also presented