Combining PCA with Neural Networks: Improving Model Efficiency and Interpretability

Authors: Sandeep Yadav

DOI: https://doi.org/10.5281/zenodo.14209382

Short DOI: https://doi.org/g8rrhb

Country: USA

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Abstract: Principal Component Analysis (PCA) and neural networks are widely used in machine learning, yet they are often applied separately—PCA for dimensionality reduction and neural networks for complex pattern recognition. This paper explores the synergy of combining PCA with neural networks to improve model efficiency and interpretability. By reducing the dimensionality of input data through PCA, we streamline neural network architectures, lowering computational costs and training time while retaining significant information. We evaluate the performance of PCA-augmented neural networks across multiple datasets in domains such as image recognition, healthcare, and finance, analyzing the trade-offs in accuracy, interpretability, and computational efficiency. The results indicate that PCA-preprocessed networks achieve comparable or even superior accuracy with fewer parameters, making them more resource-efficient. Moreover, the PCA components offer insight into feature importance and model behavior, enhancing interpretability. This study concludes that integrating PCA with neural networks is a promising approach for building efficient, interpretable models, particularly in resource-constrained environments. The findings provide actionable guidance for practitioners on optimizing neural networks with PCA, highlighting scenarios where this combination is most advantageous.

Keywords: Principal Component Analysis (PCA), Neural Networks, Dimensionality Reduction, Model Efficiency, ModelInterpretability, Feature Importance, Computational Cost Reduction, Deep Learning, PCA-Augmented Neural Networks, Machine Learning Optimization

Paper Id: 231663

Published On: 2019-02-13

Published In: Volume 7, Issue 1, January-February 2019

Cite This: Combining PCA with Neural Networks: Improving Model Efficiency and Interpretability - Sandeep Yadav - IJIRMPS Volume 7, Issue 1, January-February 2019. DOI 10.5281/zenodo.14209382

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