Online Robust Subspace Tracking from Partial Information
원문 초록
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust $l^1$-norm cost function in order to estimate and track non-stationary subspaces when the streaming data vectors are corrupted with outliers. We apply GRASTA to the problems of robust matrix completion and real-time separation of background from foreground in video. In this second application, we show that GRASTA performs high-quality separation of moving objects from background at exceptional speeds: In one popular benchmark video example, GRASTA achieves a rate of 57 frames per second, even when run in MATLAB on a personal laptop.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Robust Matrix Completion for Discrete Rating-Scale Data
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
토론
이 논문과 연결된 포럼 글이 아직 없습니다.