The matrix lasso, which minimizes a least-squared loss function with the nuclear-norm regularization, offers a generally applicable paradigm for high-dimensional low-rank matrix estimation, but its efficiency is adversely affected by heavy-tailed distributions. This paper introduces a robust procedure ...
Statistical inference-based on robust low-rank data matrix approximation, ArtículoThe singular value decomposition is widely used to approximate data matrices with lower rank matrices. Feng and He [Ann. Appl. Stat. 3 (2009) 1634-1654] developed tests on dimensionality of the mean structure of a...
Concepts for structurally robust materials that combine low thermal expansion with high stiffness face liveness detection from a single image with sparse low rank bilinear discriminative model (2013pami)Low-Rank Matrix Approximation with Manifold Regularization Lecture 2 The rank of a matrix ...
To cope with this issue, Hubert, Rousseeuw, and Vanden Branden (2005) proposed the robust PCA which has combined the projection pursuit with robust scatter matrix estimation. By doing so, the projection pursuit technique was used to project the high-dimensional observations into low-dimension space...
r : Target rank of underlying low rank matrix. params : parameters for the algorithm .max_iter : Maximum number of iterations. (default 200) .tol : Desired Frobenius norm error. (default 1e-6) .beta_init : Parameter for thresholding at initialization. (default 4*beta) ...
In order to address this problem, we present a constrained low-rank gamut completion algorithm, which can replenish gamut from limited surfaces in an image, for robust illumination estimation. In the proposed algorithm, we first discuss why the gamut completion is actually a low-rank matrix ...
Zhang Z, Zhao K (2012) Low-rank matrix approximation with manifold regularization. IEEE Trans Pattern Anal Mach Intell 35(7):1717–1729 MathSciNet Google Scholar Cai D, He X, Han J (2011) Locally consistent concept factorization for document clustering. IEEE Trans Knowl Data Eng 23(6):...
Low tubal rank tensor sensing and robust PCA from quantized measurements 主持人:刘海峰 副教授 报告人:王建军 教授 时间:2022-09-13 19:00-21:00 地点:腾讯会议 758-640-599 单位:西南大学 摘要:Low-rank tensor Sensing (LRTS) is...
Sparse Bayesian methods for low-rank matrix estimation TSP (2012) C.Baoet al. ℓ0norm based dictionary learning by proximal methods with global convergence CVPR (2014) S.Beckeret al. TFOCS: Flexible First-order Methods for Rank Minimization ...
The low rank matrix formulation and the application of the L1-norm based robust factorization technique are the main contributions of our work. Unlike the previous quadratic optimization problem for- mulation [17] which relies on dense and accurate correspon- Figure 1. First row: A selection from...