Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion
원문 초록
Low-rank matrix completion is an important problem with extensive real-world applications. When observations are uniformly sampled from the underlying matrix entries, existing methods all require the matrix to be incoherent. This paper provides the first working method for coherent matrix completion under the standard uniform sampling model. Our approach is based on the weighted nuclear norm minimization idea proposed in several recent work, and our key contribution is a practical method to compute the weighting matrices so that the leverage scores become more uniform after weighting. Under suitable conditions, we are able to derive theoretical results, showing the effectiveness of our approach. Experiments on synthetic data show that our approach recovers highly coherent matrices with high precision, whereas the standard unweighted method fails even on noise-free data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
Linear Recursive Feature Machines provably recover low-rank matrices
Teaching Arithmetic to Small Transformers
GNMR: A provable one-line algorithm for low rank matrix recovery
토론
이 논문과 연결된 포럼 글이 아직 없습니다.