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.