Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery
Di Wu, Zechao Li, Zhikai Yu, Yi He, Xin Luo
Abstract
Wireless sensor network (WSN) is an emerging and promising developing area in the intelligent sensing field. Due to various factors like sudden sensors breakdown or saving energy by deliberately shutting down partial nodes, there are always massive missing entries in the collected sensing data from WSNs. Low-rank matrix approximation (LRMA) is a typical and effective approach for pattern analysis and missing data recovery in WSNs. However, existing LRMA-based approaches ignore the adverse effects of outliers inevitably mixed with collected data, which may dramatically degrade their recovery accuracy. To address this issue, this article innovatively proposes a latent feature analysis (LFA) based spatiotemporal signal recovery (STSR) model, named LFA-STSR. Its main idea is twofold: 1) incorporating the spatiotemporal correlation into an LFA model as the regularization constraint to improve its recovery accuracy and 2) aggregating the -norm into the loss part of an LFA model to improve its robustness to outliers. As such, LFA-STSR can accurately recover missing data based on partially observed data mixed with outliers in WSNs. To evaluate the proposed LFA-STSR model, extensive experiments have been conducted on four real-world WSNs datasets. The results demonstrate that LFA-STSR significantly outperforms the related six state-of-the-art models in terms of both recovery accuracy and robustness to outliers.