Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Rank-R Approximation of Tensors Using Image-as-Matrix Representation
 

Summary: Rank-R Approximation of Tensors Using Image-as-Matrix
Representation
Hongcheng Wang, Narendra Ahuja
Beckman Institute, University of Illinois at Urbana-Champaign, USA
{wanghc,ahuja }@vision.ai.uiuc.edu
Abstract
We present a novel multilinear algebra based ap-
proach for reduced dimensionality representation of im-
age ensembles. We treat an image as a matrix, in-
stead of a vector as in traditional dimensionality reduc-
tion techniques like PCA, and higher-dimensional data
as a tensor. This helps exploit spatio-temporal redun-
dancies with less information loss than image-as-vector
methods. The challenges lie in the computational and
memory requirements for large ensembles. Currently,
there exists a rank-R approximation algorithm which,
although applicable to any number of dimensions, is ef-
ficient for only low-rank approximations. For larger
dimensionality reductions, the memory and time costs
of this algorithm become prohibitive. We propose a

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering