Publication Date:
2019
abstract:
The PARAFAC-ALS algorithm is the most widely used procedure for
approximating arrays with a trilinear structure because it provides least squares
solutions and delivers consistent outputs. Nonetheless, it is particularly slow at
converging especially under challenging conditions, i.e. data multicollinearity, high
factors’ congruence and over-factoring. This shortcoming can be quite problematic
when dealing with three-way arrays of large dimensions.
More efficient procedures can be employed, such as ATLD, however they are far less
reliable. As an alternative, ATLD and ALS can be combined in a multi-optimization
procedure in order to increase efficiency without reducing accuracy. This novel
approach has been carried out and tested on artificial and real data.
approximating arrays with a trilinear structure because it provides least squares
solutions and delivers consistent outputs. Nonetheless, it is particularly slow at
converging especially under challenging conditions, i.e. data multicollinearity, high
factors’ congruence and over-factoring. This shortcoming can be quite problematic
when dealing with three-way arrays of large dimensions.
More efficient procedures can be employed, such as ATLD, however they are far less
reliable. As an alternative, ATLD and ALS can be combined in a multi-optimization
procedure in order to increase efficiency without reducing accuracy. This novel
approach has been carried out and tested on artificial and real data.
Iris type:
4.1 Contributo in Atti di convegno
Keywords:
ATLD, computational efficency, CP model, trilinear data
List of contributors:
Gallo, Michele; Simonacci, Violetta; Guarino, Massimo
Book title:
Smart Statistics for Smart Applications