The following pages link to (Q3444931):
Displaying 50 items.
- Convergence of a second order Markov chain (Q279270) (← links)
- Exact low-rank matrix completion from sparsely corrupted entries via adaptive outlier pursuit (Q368099) (← links)
- Approximation of rank function and its application to the nearest low-rank correlation matrix (Q386463) (← links)
- Low-rank incremental methods for computing dominant singular subspaces (Q413537) (← links)
- Detecting low-rank clusters via random sampling (Q425614) (← links)
- Practical acceleration for computing the HITS expertrank vectors (Q442725) (← links)
- A gradient based iterative solutions for Sylvester tensor equations (Q474659) (← links)
- Self-adjoint fourth order differential operators with eigenvalue parameter dependent and periodic boundary conditions (Q518388) (← links)
- Matrix rank and inertia formulas in the analysis of general linear models (Q520137) (← links)
- Efficient methods for grouping vectors into low-rank clusters (Q550984) (← links)
- Weighted Moore-Penrose inverses and fundamental theorem of even-order tensors with Einstein product (Q721456) (← links)
- On extremum properties of orthogonal quotients matrices (Q847212) (← links)
- Low rank matrix completion by alternating steepest descent methods (Q905914) (← links)
- The MM alternative to EM (Q906520) (← links)
- PCA and SVD with nonnegative loadings (Q955823) (← links)
- A singular value decomposition algorithm based on solving hyperplane constrained nonlinear systems (Q969142) (← links)
- Magic square spectra (Q1017627) (← links)
- Sparsest factor analysis for clustering variables: a matrix decomposition approach (Q1630876) (← links)
- Subspace learning for unsupervised feature selection via matrix factorization (Q1677021) (← links)
- The Drazin inverse of an even-order tensor and its application to singular tensor equations (Q1732551) (← links)
- The two-stage iteration algorithms based on the shortest distance for low-rank matrix completion (Q1740089) (← links)
- Integer matrix approximation and data mining (Q1747623) (← links)
- Krylov-type methods for tensor computations.I (Q1931774) (← links)
- Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm (Q1946921) (← links)
- Interval tensors and their application in solving multi-linear systems of equations (Q2004549) (← links)
- Toeplitz matrix completion via smoothing augmented Lagrange multiplier algorithm (Q2009379) (← links)
- Numerical study on Moore-Penrose inverse of tensors via Einstein product (Q2041532) (← links)
- A new method based on the manifold-alternative approximating for low-rank matrix completion (Q2061479) (← links)
- Triangular decomposition of CP factors of a third-order tensor with application to solving nonlinear systems of equations (Q2067307) (← links)
- A semi-smoothing augmented Lagrange multiplier algorithm for low-rank Toeplitz matrix completion (Q2067806) (← links)
- Efficient proximal mapping computation for low-rank inducing norms (Q2073049) (← links)
- A Krylov-Schur-like method for computing the best rank-\((r_1,r_2,r_3)\) approximation of large and sparse tensors (Q2084262) (← links)
- Tensor-train decomposition for image recognition (Q2174198) (← links)
- Guarantees of Riemannian optimization for low rank matrix completion (Q2176515) (← links)
- An alternating minimization method for matrix completion problems (Q2182816) (← links)
- Accelerated low rank matrix approximate algorithms for matrix completion (Q2203735) (← links)
- A singular value thresholding with diagonal-update algorithm for low-rank matrix completion (Q2217856) (← links)
- Factor analysis of ordinal data via decomposition of matrices with grades (Q2254612) (← links)
- Fundamental conditions on the sampling pattern for union of low-rank subspaces retrieval (Q2294578) (← links)
- Spanning tree packing number and eigenvalues of graphs with given girth (Q2321370) (← links)
- Semi-sparse PCA (Q2331152) (← links)
- A low-rank tensor-based algorithm for face recognition (Q2337553) (← links)
- SVD based initialization: A head start for nonnegative matrix factorization (Q2469730) (← links)
- Implicit steepest descent algorithm for optimization with orthogonality constraints (Q2673531) (← links)
- Hybrid iterative refined restarted Lanczos bidiagonalization methods (Q2679823) (← links)
- MANOVA, LDA, and FA criteria in clusters parameter estimation (Q2813506) (← links)
- Guarantees of Riemannian optimization for low rank matrix recovery (Q2818273) (← links)
- Mining order-preserving submatrices from probabilistic matrices (Q2943568) (← links)
- Approximating Matrix Multiplication for Pattern Recognition Tasks (Q4238529) (← links)
- Computing Low-Rank Approximations of Large-Scale Matrices with the Tensor Network Randomized SVD (Q4584919) (← links)
