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Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis

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@article{IJASEIT5677,
   author = {Rolysent K Paredes and Ariel M Sison and Ruji P Medina},
   title = {Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {3},
   year = {2018},
   pages = {963--969},
   keywords = {artificial neural network; bayesian regularization back propagation; generalized singular value decomposition; ANN for LDA/GSVD; linear discriminant analysis.},
   abstract = {

Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5677},    doi = {10.18517/ijaseit.8.3.5677} }

EndNote

%A Paredes, Rolysent K
%A Sison, Ariel M
%A Medina, Ruji P
%D 2018
%T Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
%B 2018
%9 artificial neural network; bayesian regularization back propagation; generalized singular value decomposition; ANN for LDA/GSVD; linear discriminant analysis.
%! Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
%K artificial neural network; bayesian regularization back propagation; generalized singular value decomposition; ANN for LDA/GSVD; linear discriminant analysis.
%X 

Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5677 %R doi:10.18517/ijaseit.8.3.5677 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 3 %@ 2088-5334

IEEE

Rolysent K Paredes,Ariel M Sison and Ruji P Medina,"Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 3, pp. 963-969, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.3.5677.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Paredes, Rolysent K
AU  - Sison, Ariel M
AU  - Medina, Ruji P
PY  - 2018
TI  - Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 3
Y2  - 2018
SP  - 963
EP  - 969
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - artificial neural network; bayesian regularization back propagation; generalized singular value decomposition; ANN for LDA/GSVD; linear discriminant analysis.
N2  - 

Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5677 DO - 10.18517/ijaseit.8.3.5677

RefWorks

RT Journal Article
ID 5677
A1 Paredes, Rolysent K
A1 Sison, Ariel M
A1 Medina, Ruji P
T1 Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 3
YR 2018
SP 963
OP 969
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 artificial neural network; bayesian regularization back propagation; generalized singular value decomposition; ANN for LDA/GSVD; linear discriminant analysis.
AB 

Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5677 DO - 10.18517/ijaseit.8.3.5677