A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Tensors – Big Data in Multidimensional Settings
Tensors appear to be an appropriate way to represent high‐dimensional data and their interdependences in railway track infrastructure. Tensor factorization and decomposition are becoming major tools for large multidimensional data analysis. The application of the tensor, apart from addressing the previous shortcomings, will provide a platform for performing data mining applications. The two most widely used tensor decomposition models are the Tucker model and the canonical decomposition (CANDECOMP) model. The aim of nonnegative tensor is to extract the data‐dependent nonnegative basis function, and the target data can be expressed by the linear and non‐linear combination of the nonnegative components. To effectively interpret rail geometry data in a multidimensional approach, the use of 2D analysis of railway data fails to address the proper influence of different parameters in time after maintenance and the interaction of different variables in time.
Tensors – Big Data in Multidimensional Settings
Tensors appear to be an appropriate way to represent high‐dimensional data and their interdependences in railway track infrastructure. Tensor factorization and decomposition are becoming major tools for large multidimensional data analysis. The application of the tensor, apart from addressing the previous shortcomings, will provide a platform for performing data mining applications. The two most widely used tensor decomposition models are the Tucker model and the canonical decomposition (CANDECOMP) model. The aim of nonnegative tensor is to extract the data‐dependent nonnegative basis function, and the target data can be expressed by the linear and non‐linear combination of the nonnegative components. To effectively interpret rail geometry data in a multidimensional approach, the use of 2D analysis of railway data fails to address the proper influence of different parameters in time after maintenance and the interaction of different variables in time.
Tensors – Big Data in Multidimensional Settings
Attoh‐Okine, Nii O. (author)
Big Data and Differential Privacy ; 157-173
2017-06-26
17 pages
Article/Chapter (Book)
Electronic Resource
English
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