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
A PARAFAC-ALS variant for fitting large datasets / Gallo, M; Simonacci, V; Guarino, M. - (2019), pp. 895-899. (Intervento presentato al convegno SIS2019: Smart Statistics for Smart Applications tenutosi a Milano nel 18-21 June 2019).
A PARAFAC-ALS variant for fitting large datasets
Simonacci V;
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 dataFile | Dimensione | Formato | |
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