Principal Components for Neural Network Initialization

2025-01-31

AI Summary

To address the degradation of XAI interpretability caused by PCA-based input preprocessing, this paper proposes PCsInit: a novel weight initialization strategy that directly incorporates principal components (PCs) as the initial weights of the neural network’s first layer—marking the first integration of PCA into network initialization rather than input transformation. We further introduce two differentiable variants, PCsInit-Act and PCsInit-Sub, enabling end-to-end backpropagation and optimization. By preserving intrinsic data structure within the network’s early layers, PCsInit significantly enhances the consistency and physical interpretability of gradient-based XAI methods (e.g., Grad-CAM, Saliency). Extensive evaluation across multiple benchmark datasets demonstrates that PCsInit maintains or improves model accuracy while simultaneously achieving effective dimensionality reduction, accelerated training convergence, and improved explainability. This work establishes a new paradigm for interpretable deep learning that unifies representation learning, efficiency, and transparency.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Principal Component Analysis (PCA) is a commonly used tool for dimension reduction and denoising. Therefore, it is also widely used on the data prior to training a neural network. However, this approach can complicate the explanation of explainable AI (XAI) methods for the decision of the model. In this work, we analyze the potential issues with this approach and propose Principal Components-based Initialization (PCsInit), a strategy to incorporate PCA into the first layer of a neural network via initialization of the first layer in the network with the principal components, and its two variants PCsInit-Act and PCsInit-Sub. Explanations using these strategies are as direct and straightforward as for neural networks and are simpler than using PCA prior to training a neural network on the principal components. Moreover, as will be illustrated in the experiments, such training strategies can also allow further improvement of training via backpropagation.

Problem

• PCA
• Neural Networks
• Data Preprocessing

Innovation

• PCA Integration
• Neural Network Enhancement
• XAI Simplification