Hybrid Models Integrating Tensor Learning with Conventional Machine Learning for Enhanced Predictive Analytics

Modern data-driven applications require sophisticated analytical techniques capable of handling increasingly complex and high-dimensional datasets. While conventional machine learning models have achieved significant success in various domains, their performance often plateaus when confronted with large-scale, multi-modal, and correlated data. Tensor learning, which extends matrix-based representations to higher-order data structures, provides a powerful framework for modelling such data. However, standalone tensor methods can be challenging to integrate into existing machine learning pipelines due to issues such as computational complexity and interpretability. This paper proposes hybrid models that fuse the representational strengths of tensor learning with the predictive power and established frameworks of conventional machine learning techniques. By leveraging tensor decomposition and factorization methods, these hybrid approaches can exploit latent structures and shared information across multiple dimensions. This integration can enhance predictive accuracy, improve scalability, and maintain interpretability in real-world applications. We present a comprehensive review of existing tensor decomposition methods and examine how these can be integrated with conventional machine learning algorithms, including deep neural networks, kernel methods, and ensemble models. The paper details our methodology for constructing hybrid models and provides extensive experimentation on multiple datasets to analyze the predictive improvements. Results suggest that hybrid tensor-machine learning models consistently outperform both standalone conventional methods and pure tensor-based models, demonstrating improved accuracy, robustness, and scalability. The key contributions of this work are the development of novel hybrid architectures, a rigorous experimental evaluation framework, and guidelines for practitioners to adopt such methods for advanced predictive analytics.