Beschreibung
The capability of the classical Linear Discriminant Analysis based on Generalized Singular Value Decomposition (LDA/GSVD) deteriorates when dealing with unlabeled datasets because LDA requires predefined inputs and targets. In addition, the LDA/GSVD algorithm suffers from high computation cost due to its complex mathematical calculations and iterations. To address these problems, this study introduces Self-Organizing Map (SOM) as a new method in labeling datasets, and the development of an Artificial Neural Network-based algorithm to overcome the computational cost of LDA/GSVD. The results show that using SOM and ANN are effective in solving the problems of the traditional LDA/GSVD algorithm.
Autorenporträt
Rolysent Paredes is a faculty member of Misamis University in Ozamiz City, Philippines. He is a Cisco-certified Academy Instructor. He has several publications under his name and has presented researchers on data mining, artificial intelligence, machine learning, and computer networks in various international conferences.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
