Momentum-Accelerated and Biased Unconstrained Non-Negative Latent Factor Model for Handling High-Dimensional and Incomplete Data

High-dimensional and incomplete (HDI) data are involved frequently in big data-related industrial applications. Latent factor (LF) analysis aims at extracting the knowledge of great value from such extremely sparse HDI data efficiently. Non-negative LF models based on the single LF-dependent, non-negative, and multiplicative update rules exactly are the representative of LF analysis. However, these models face low generalization dilemma due to incompatible with general unconstrained optimization techniques. To address this issue, this article proposes a novel momentum-accelerated and biased unconstrained non-negative latent factor (MBUNLF) model, which matches with unconstrained optimization techniques. The proposed MBUNLF model is built on three main ideas: (a) Improving the generalization through a non-negative mapping function; (b) Capturing information among different entities through linear biases; (c) Accelerating convergence during the training process through generalized momentum method. Empirical studies on six datasets from industrial applications indicate that the proposed MBUNLF model outperforms nine state-of-the-art models when processing HDI data, reducing the root mean square error by 19.47% on average. It demonstrates the validity of the MBUNLF model in extracting non-negative LFs from HDI data.