A hybrid deep compression model for enhanced classification of high-dimensional image data

Abstract High-dimensional image data often contain redundant pixel-level information, leading to increased computational complexity and reduced learning efficiency in machine learning models. To address this challenge, we propose a Hybrid Compressive Feature Reduction Model (HCFRM) that achieves an effective trade-off between computational efficiency and classification accuracy on challenging large-scale datasets such as CIFAR-10, using CSV-shaped image representations. The proposed HCFRM framework employs an optimized configuration consisting of 100 PCA components, a 64-dimensional Autoencoder latent space, and the top 32 Mutual Information-selected features to achieve an effective balance between dimensionality reduction and classification performance. This optimized HCFRM configuration was adopted throughout all experiments to ensure consistent and reproducible evaluation across the benchmark datasets. This adaptive tuning ensures robust performance on flattened high-dimensional representations, demonstrated on CIFAR-10, medical imaging, and hyperspectral datasets, highlighting the model’s flexibility and applicability to various image domains. The decision-making process is guided by the analysis of the expected values of feature vectors projected into a reduced coordinate space through a hybrid sensor processing pipeline. Experimental results demonstrate that HCFRM achieves a 74% reduction in feature dimensionality while providing approximately 28–38% faster training compared with PCA+Autoencoder and Autoencoder-only baselines. Despite aggressive parameter-space compaction, the proposed model attains a classification accuracy of 74%, indicating effective class separability under reduced computational cost. These findings confirm that HCFRM is an efficient and scalable solution for learning large-scale, high-dimensional image data, making it well suited for memory-efficient and high-performance visual learning systems. The proposed framework further enables efficient processing of high-dimensional visual representations for 3D scene reconstruction and volumetric modeling, supporting enhanced computer vision performance. The modular and compressive design of HCFRM makes it suitable for scalable and cluster-based visual learning systems.