A Novel Matrix Completion Model Based on the Multi-Layer Perceptron Integrating Kernel Regularization

Abstract

Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of noise. Therefore, this paper proposes a novel matrix completion model based on the MLP integrating kernel regularization (MCKR). The proposed model uses the MLP to extract feature, which can automatically extract interaction feature of missing data and observational data. Moreover, through a non-linear mapping of low-dimensional latent subspace and schatten p-norm, a novel kernel regularization is derived to provide a global constraint for reducing the noise of the completed matrix. Finally, the validity and practicability of the proposed model is verified on three publicly available datasets. Compared with baselines including matrix factorization (MF), Logistic matrix factorization (LMF), expertise matrix factorization (EMF) and multi-layer perceptron (MLP), the proposed model achieves state-of-the-art result.

Keywords

• Matrix completion
• Kernel regularization
• non-linear
• Schatten p-norm
• multi-layer perceptron