Tensor analysis methods for activity characterization in spatiotemporal data | Zendy

Michael Joseph Haass | Zendy; Mark Van Benthem | Zendy; Edward M Ochoa | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Tensor analysis methods for activity characterization in spatiotemporal data

Author(s) -

Michael Joseph Haass,

Mark Van Benthem,

Edward M Ochoa

Publication year - 2014

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1200656

Subject(s) - matrix decomposition , computer science , tensor (intrinsic definition) , decomposition , matrix (chemical analysis) , factorization , computation , characterization (materials science) , basis (linear algebra) , theoretical computer science , tensor algebra , algorithm , data mining , artificial intelligence , algebra over a field , mathematics , pure mathematics , ecology , current algebra , eigenvalues and eigenvectors , physics , materials science , geometry , quantum mechanics , jordan algebra , composite material , biology , nanotechnology

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research