Developing an Artificial Neural Network algorithm for Generalized Singular Value Decomposition-based 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.