Comparison of machine learning algorithms for classification of Big Data sets

Abstract

This article analyzes and compares various Quantum machine learning algorithms on big data. The main contribution of this article is to provide a new machine-learning approach using Quantum computing for big data analysis with features of robust, novel, and effective Quantum computing. This work proposes a global Quantum feature extraction technique for large-scale image classification based on Schmidt decomposition for the first time. Additionally, a new version of the Quantum learning algorithm is presented, which uses the features of Hamming distance to classify images. With the help of algorithm analysis and experimental findings from the benchmark database Caltech 101, a successful method for large-scale image classification is developed and put forth in the context of big data. The proposed model yields an average accuracy of 98% with the proposed enhanced Quantum classifier, QeSVM classifier, swarm particle optimizer with Twin wave SVM, QPSO-TWSVM, and other Q-CNN models on different Big Data sets.