TY - JOUR
AU - Wang, Yugang
AU - Elhamifar, Ehsan
PY - 2018/04/29
Y2 - 2023/04/01
TI - High Rank Matrix Completion With Side Information
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 32
IS - 1
SE - AAAI Technical Track: Machine Learning
DO - 10.1609/aaai.v32i1.11809
UR - https://ojs.aaai.org/index.php/AAAI/article/view/11809
SP -
AB - <p> We address the problem of high-rank matrix completion with side information. In contrast to existing work dealing with side information, which assume that the data matrix is low-rank, we consider the more general scenario where the columns of the data matrix are drawn from a union of low-dimensional subspaces, which can lead to a high rank matrix. Our goal is to complete the matrix while taking advantage of the side information. To do so, we use the self-expressive property of the data, searching for a sparse representation of each column of matrix as a combination of a few other columns. More specifically, we propose a factorization of the data matrix as the product of side information matrices with an unknown interaction matrix, under which each column of the data matrix can be reconstructed using a sparse combination of other columns. As our proposed optimization, searching for missing entries and sparse coefficients, is non-convex and NP-hard, we propose a lifting framework, where we couple sparse coefficients and missing values and define an equivalent optimization that is amenable to convex relaxation. We also propose a fast implementation of our convex framework using a Linearized Alternating Direction Method. By extensive experiments on both synthetic and real data, and, in particular, by studying the problem of multi-label learning, we demonstrate that our method outperforms existing techniques in both low-rank and high-rank data regimes. </p>
ER -