We denote the family of all non-zero rank-commutators of L by L. One of the results we obtained is that Sn = Sn, where Sn is the family of all symmetric n x n matrices. It is still unknown whether there is another family L = Sn satisfying L = L.Fengming Dong...
Our proposed algorithm utilizes the known part of the support to recover a matrix as the sum of a low-rank matrix and a sparse component and is tested on the problem of surveillance video reconstruction from compressive measurements. Our experimental results show that the incorporation of partial ...
Our experimental results illustrate that our new frameworks are more effective and accurate when compared with other methods. 展开 关键词: Recovery of matrix Low-rank and sparse decomposition Truncated nuclear norm Surveillance video Removing shadows of image ...
Matrix Table Question Text Entry Question Form Field Question Slider Question Rank Order Question Side by Side Question Autocomplete Questions Specialty Questions Advanced Questions Pre-Made Qualtrics Library Questions Formatting Questions Formatting Answer Choices Page Breaks Response Requirements & Validation...
The system is said to be in a mixed state if its density matrix is mixed, i.e., not a rank one projection. 4.0.1 Properties of the density matrix The density matrixρ has the following properties: 1. It is self-adjoint: ρ = ρ†. 2. It is positive-semidefinite: ρ≥ 0 i....
Constituent matrices have rank 1. 2. The product of two constituent matrices is ZiZj={Zi,i=j0,i≠j Raising Zi to any power gives the matrix Zi. Zi is said to be idempotent. 3. The sum of the n constituent matrices of an n×n matrix is equal to the identity matrix ∑i=1nZi=In...
被引量: 0发表: 2025年 Low rank matrix recovery with adversarial sparse noise* Many problems in data science can be treated as recovering a low-rank matrix from a small number of random linear measurements, possibly corrupted with adv... H Xu,S Li,J Lin - 《Inverse Problems》 被引量: ...
被引量: 0发表: 2019年 Non-negative enhanced discriminant matrix factorization method with sparsity regularization Non-negative matrix factorizationFace recognitionFeature extractionData representationDiscriminant informationEfficient low-rank representation of data plays a significan... M Tong,H Bu,M Zhao,....
In high-dimensional models with p≥ n, the sample covariance matrix S is rank-deficient. Estimation of Σ and Ω also suffer from the curse of dimensionality even when p < n with p/n→ c∈ (0, 1). When p→ ∞, S is no longer consistent for Σ. Estimation of Σ was not an ...
Non-convex low-rank representation (NLRR) via matrix factorization is one of the state-of-the-art techniques for subspace clustering. However, NLRR cannot scale to problems with large n (number of samples) as it requires either the inversion of an n脳n matrix or solving an n脳n linear ...