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<p>Feature fusion is essential for enhancing the performance of machine learning classifiers, particularly when managing heterogeneous, high-dimensional, and multimodal datasets. In this work, we propose Weighted PCA with Adaptive Concatenation and Dynamic Scaling (WPCA-ACDS)<b>,</b> a novel feature fusion technique designed to address challenges such as overfitting, high dimensionality, and noise sensitivity. WPCA-ACDS integrates three key components: Weighted Principal Component Analysis (Weighted PCA) for efficient dimensionality reduction, Adaptive Concatenation for optimal feature selection based on data-driven strategies, and Dynamic Scaling to balance feature contributions and mitigate the impact of outliers or irrelevant features. Through extensive empirical evaluation on five benchmark datasets—CIFAR-10, Caltech-101, Scene-15, MNIST, and Oxford Pets—utilizing five classifiers (SVM, Random Forest, KNN, Logistic Regression, and Decision Trees), we demonstrate that WPCA-ACDS outperforms several state-of-the-art fusion techniques, including Simple Concatenation<b>,</b> PCA-based Concatenation<b>,</b> Average Fusion<b>,</b> Weighted Average Fusion<b>,</b> Product-based Fusion<b>,</b> cv-weight<b>,</b> Multiple Kernel Learning (MKL)<b>,</b> Collaborative Boosting and Dominant Set Fusion<b>.</b> WPCA-ACDS excels in terms of classification accuracy<b>,</b> robustness to noise and high-dimensional data<b>,</b> and computational scalability<b>.</b> Additionally, sensitivity and trade-off analyses emphasize WPCA-ACDS's versatility, solidifying its position as a robust and scalable solution for modern machine learning tasks. Ablation studies further reveal the critical role of each component, demonstrating that the full WPCA-ACDS framework consistently outperforms all ablated variants in classification performance. This establishes WPCA-ACDS as an effective and comprehensive feature fusion technique for a wide range of machine learning tasks.</p>

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A unified and scalable machine learning framework for feature fusion in object classification using weighted PCA with adaptive concatenation and dynamic scaling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月10日

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  • Amitav Mahapatra,
  • Prashanta Kumar Patra

摘要

Feature fusion is essential for enhancing the performance of machine learning classifiers, particularly when managing heterogeneous, high-dimensional, and multimodal datasets. In this work, we propose Weighted PCA with Adaptive Concatenation and Dynamic Scaling (WPCA-ACDS), a novel feature fusion technique designed to address challenges such as overfitting, high dimensionality, and noise sensitivity. WPCA-ACDS integrates three key components: Weighted Principal Component Analysis (Weighted PCA) for efficient dimensionality reduction, Adaptive Concatenation for optimal feature selection based on data-driven strategies, and Dynamic Scaling to balance feature contributions and mitigate the impact of outliers or irrelevant features. Through extensive empirical evaluation on five benchmark datasets—CIFAR-10, Caltech-101, Scene-15, MNIST, and Oxford Pets—utilizing five classifiers (SVM, Random Forest, KNN, Logistic Regression, and Decision Trees), we demonstrate that WPCA-ACDS outperforms several state-of-the-art fusion techniques, including Simple Concatenation, PCA-based Concatenation, Average Fusion, Weighted Average Fusion, Product-based Fusion, cv-weight, Multiple Kernel Learning (MKL), Collaborative Boosting and Dominant Set Fusion. WPCA-ACDS excels in terms of classification accuracy, robustness to noise and high-dimensional data, and computational scalability. Additionally, sensitivity and trade-off analyses emphasize WPCA-ACDS's versatility, solidifying its position as a robust and scalable solution for modern machine learning tasks. Ablation studies further reveal the critical role of each component, demonstrating that the full WPCA-ACDS framework consistently outperforms all ablated variants in classification performance. This establishes WPCA-ACDS as an effective and comprehensive feature fusion technique for a wide range of machine learning tasks.