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

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

Abstract 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...

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Bibliographic Details
Main Authors: Amitav Mahapatra, Prashanta Kumar Patra
Format: Article
Language:English
Published: Springer 2025-06-01
Series:Discover Computing
Subjects:
Weighted PCA
Adaptive Concatenation
Dynamic Scaling
Feature Fusion
Object Classification
Machine Learning
Online Access:https://doi.org/10.1007/s10791-025-09622-1
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https://doi.org/10.1007/s10791-025-09622-1

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