Robust Neural Tucker Factorization with Bias Correction and Adaptive Initialization

2026-06-15 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

KaBiN, a novel neural Tucker factorization model, addresses challenges in high-dimensional incomplete (HDI) tensor completion, particularly in traffic and climate applications. Conventional methods struggle with sparse observations, non-linear dynamics, and non-stationary variations. While neural Tucker factorization models high-order interactions, its performance is often hampered by suboptimal parameter initialization and bias configuration. KaBiN mitigates these issues by employing Kaiming uniform initialization for embedding and Tucker linear parameters, alongside a simple bias correction in the output mapping. This approach effectively decouples global mean shifts from local structural representations, creating a stable and well-conditioned optimization landscape. Experiments on three real-world HDI tensor datasets demonstrate KaBiN's superior performance over the original NeuTucF with minimal computational overhead.

Key takeaway

For Machine Learning Engineers optimizing high-dimensional incomplete tensor completion models, consider integrating KaBiN's techniques. Your models can achieve better performance and stability by implementing Kaiming uniform initialization for embeddings and Tucker linear parameters, and applying a simple bias correction in the output mapping. This approach directly addresses common issues like gradient saturation and global statistical deviations, leading to more robust and accurate results in applications like traffic or climate modeling.

Key insights

KaBiN improves neural Tucker factorization for HDI tensor completion via Kaiming initialization and bias correction.

Principles

• Suboptimal initialization causes gradient saturation.
• Omitted bias parameters absorb global deviations.
• Decoupling global mean shifts stabilizes optimization.

Method

KaBiN uses Kaiming uniform initialization for embedding and Tucker linear parameters, plus a simple bias correction in the output mapping to stabilize optimization.

In practice

• Apply to traffic data tensor completion.
• Use for climate application tensors.

Topics

• Neural Tucker Factorization
• Tensor Completion
• Kaiming Initialization
• Bias Correction
• High-Dimensional Tensors
• Machine Learning

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.