The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion
This addresses matrix completion for applications like image reconstruction and recommender systems, but appears incremental as it builds on existing PCA-based methods.
The paper tackles the problem of computationally intensive matrix completion methods that struggle with heterogeneous data by proposing a sparse reverse of principal component analysis, which achieves accurate missing data estimates with controllable convergence in iterations.
Matrix completion constantly receives tremendous attention from many research fields. It is commonly applied for recommender systems such as movie ratings, computer vision such as image reconstruction or completion, multi-task learning such as collaboratively modeling time-series trends of multiple sensors, and many other applications. Matrix completion techniques are usually computationally exhaustive and/or fail to capture the heterogeneity in the data. For example, images usually contain a heterogeneous set of objects, and thus it is a challenging task to reconstruct images with high levels of missing data. In this paper, we propose the sparse reverse of principal component analysis for matrix completion. The proposed approach maintains smoothness across the matrix, produces accurate estimates of the missing data, converges iteratively, and it is computationally tractable with a controllable upper bound on the number of iterations until convergence. The accuracy of the proposed technique is validated on natural images, movie ratings, and multisensor data. It is also compared with common benchmark methods used for matrix completion.