High-dimensional incomplete (HDI) tensors are widely used in traffic and climate applications, but sparse observations make accurate completion difficult. The intrinsic non-linear dynamics and non-stationary variations across distinct multi-modal fields severely hinder the efficacy of conventional linear reconstruction frameworks. Neural Tucker factorization provides an effective framework for modeling high-order interactions among tensor modes. By parameterizing underlying structural characteristics into continuous latent spaces, neural representations circumvent the rigid low-rank constraints of classical algebra. However, its performance can still be affected by implementation-level choices, especially parameter initialization and the bias configuration of the final output mapping. Suboptimal initializations frequently lead to variance explosion across the cubically expanded interaction spaces, driving the subsequent non-linear activation boundaries into severe gradient saturation zones, while the omission of a dedicated translation parameter forces interaction weights to implicitly absorb global statistical deviations. This paper proposes a simple yet effective neural Tucker factorization model with Kaiming initialization and bias correction (KaBiN) for HDI tensor completion. The proposed model utilizes Kaiming uniform initialization for the embedding and Tucker linear parameters, and adopts a simple bias correction in output mapping. By elegantly decoupling global mean shifts from local structural representations, the framework provides a highly stable and well-conditioned optimization landscape. Experiments on three real-world HDI tensor datasets show that KaBiN achieves better performance than the original NeuTucF, while introducing minimal computational overhead.

翻译：高维不完备张量在交通与气候领域应用广泛，但稀疏观测数据导致准确补全面临挑战。跨多模态场的固有非线性动力学与非平稳变化严重阻碍了传统线性重建框架的有效性。神经Tucker分解为建模张量模式间的高阶交互提供了有效框架。通过将底层结构特征参数化到连续隐空间中，神经表示规避了经典代数中刚性低秩约束的限制。然而，其性能仍受实现层面选择的影响，尤其体现在参数初始化与最终输出映射的偏差配置上。次优初始化常导致立方扩展交互空间中的方差爆炸，使后续非线性激活边界陷入严重梯度饱和区域；而专用平移参数的缺失迫使交互权重隐式吸收全局统计偏差。本文提出一种简洁高效的神经Tucker分解模型——基于Kaiming初始化与偏差校正的HDI张量补全方法，它采用Kaiming均匀初始化嵌入与Tucker线性参数，并在输出映射中引入简易偏差校正。通过优雅解耦全局均值偏移与局部结构表征，该框架提供高度稳定且良态的优化地形。在三个真实世界HDI张量数据集上的实验表明，KaBiN在引入极小计算开销的同时，取得了优于原始NeuTucF的性能表现。