Model Based Image Classification Using Dimentionality Reduction

Abstract

Image classification is a fundamental task in machine learning, with applications in medical

imaging, remote sensing, and autonomous systems. However, the increasing complexity

and high-dimensional nature of image datasets pose significant challenges for conventional

classification models. To address these limitations, this study explores a novel

approach based ondimensionality reduction with variational encoders to enhance feature

representation and improve classification accuracy. Variational autoencoders (VAEs) have

emerged as a powerful tool for reducing dimensionality while preserving essential features.

In this work, we extend VAEs with quantum-enhanced encoding techniques using subsystem

purification, enabling efficient data compression while mitigating information loss.

By leveraging hybrid quantum-classical architectures, we propose a framework that integrates

quantum variational encoders with classical neural networks, optimizing feature

extraction for classification tasks. Our approach is evaluated on benchmark datasets, including

Iris and MNIST, demonstrating improved accuracy and computational efficiency

compared to traditional dimensionality reduction techniques. The results show that our

model achieves superior performance by maintaining discriminative features while reducing

data complexity. Furthermore, we address key challenges such as barren plateaus

and optimization instability through time-nonlocal optimization strategies. This research

contributes to the advancement of quantum-enhanced machine learning by introducing

a robust and scalable dimensionality reduction framework for image classification. The

proposed method sets a new standard for efficient quantum-classical hybrid models, offering

a promising direction for future developments in quantum-assisted data processing

and classification.