A matrix completion-based multiview learning method for imputing missing values in buoy monitoring data

Abstract

Buoy systems have been deployed to monitor coastal waters, and these systems generate massive amounts of data with high temporal resolution. In practice, however, many of these records are often lost, which adversely impacts further data analysis. Current imputation methods place more emphasis on spatial and temporal correlations but ignore the crucial interactions between different parameters. To fully employ these interactions and further improve the imputation performance, a matrix completion-based multiview learning (MC-MVL) method is proposed in this paper to fill in the missing values in buoy monitoring data. MC-MVL considers three hybrid views, i.e., the temporal-parameter view, the spatial-parameter view and the spatiotemporal view, and each view is formulated as a matrix. The fixed-point continuation with approximate singular value decomposition (FPCA), the non-convex (Non-Convex) and the inexact augmented Lagrange multipliers (IALM) algorithms are separately utilized to reconstruct these three matrices. Then, a ridge regression-based multiview learning algorithm is used to aggregate the estimates of the three views into the final results. We used a buoy monitoring dataset from the Zhejiang coastal area to verify the imputation ability of the proposed model. The results confirm that MC-MVL achieves better performance than do 8 baseline approaches.