[PDF] Top 20 Lower bounds for the low rank matrix approximation
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Lower bounds for the low rank matrix approximation
... the low-rank component and E ∈ R m×n is the perturbation compo- nent of ...the matrix E is small, the classical principal components analysis (PCA [–]) can seek the best rank-r estimation ... See full document
14
Clustering Over Multiple Evolving Data Streams of the Traffic Cyber-Physical Systems
... r×n matrix consisting of r randomly picked rows of A, and U is a c×r matrix computed from C and ...middle matrix based on these two ...the matrix and allows computations to be performed more ... See full document
16
INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 4 0 DOMAIN DELIVERY SUPPORT AND MONITORING EVALUATION
... the low-rank approximation problem where the low-rank matrix is formed by the nonlocal similarity ...the low-rank optimization ...the low-rank ... See full document
11
The lower bounds for the rank of matrices and some sufficient conditions for nonsingular matrices
... the lower bound for the rank of the matrix are obtained, such as theorems and corollaries in Section ...the matrix being closely related to the lower bound for the rank of the ... See full document
8
On the equivalence between Total Least Squares and Maximum Likelihood PCA
... weighted low rank matrix approximation where the weight is derived from the distribution of the errors in the ...weighted low rank approximation (WLRA), the MLPCA, the ... See full document
14
Computing Approximation GCD of Several Polynomials by Structured Total Least Norm
... that approximation GCD of se- veral polynomials can be solved by a practical and re- liable way based on STLN method and transformed to the approximation of Sylvester structure ...structured matrix ... See full document
8
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise
... approximate low-rank matrix recovery using an M- estimator that is a combination of a data term, and a weighted nuclear norm as a ...a matrix (Horn and Johnson, 1985), and has been studied in ... See full document
33
Optimal Estimation of Low Rank Density Matrices
... minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, in the case of Pauli measurements) ... See full document
36
Completing Any Low-rank Matrix, Provably
... This approach of adjusting the sampling distribution based on leverage scores is relevant whenever we have some freedom in choosing the observed entries. For example, many recommendation systems do actively solicit ... See full document
36
Sparse and smooth canonical correlation analysis through rank 1 matrix approximation
... In Figs. 6 and 7, the normalized mean square error is plotted versus the SNR for the proposed approaches and state of the art algorithm. It is clearly shown that our sparse CCA based on rank-1 matrix ... See full document
14
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
... common low-rank as- sumption - that is, under the model assumption (or approximation) that there is an under- lying complete matrix of some low rank r from which the partial ... See full document
46
Eigenvalues of the resistance distance matrix of complete multipartite graphs
... resistance-distance matrix of complete multipartite ...distance matrix of complete multipartite graphs. In Section , we give some lower and upper bounds on the largest eigenvalue of the ... See full document
11
Determinant approximations
... perturbation bounds for determinants that involve the condition number of the matrix are given in [4, Problem ...[7] lower and upper bounds for the determinant are presented for matrices whose ... See full document
13
Explicit Polynomial Sequences with Maximal Spaces of Partial Derivatives and a Question of K. Mulmuley
... upper bounds for the utility of Koszul flattenings, another variant of the partial derivatives map, in comparing the complexity of polynomials (Proposition ...these bounds are sharp for the first Koszul ... See full document
24
Global denoising for 3D MRI
... The primary concern for efficiently generating a low-rank matrix approximation using the Nyström method is the sampling scheme. In this study, a k-means clustering algo- rithm was proposed as ... See full document
17
Spatial Aggregation Facilitates Discovery of Spatial Topics
... through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved ... See full document
11
Diophantine approximation on manifolds and lower bounds for Hausdorff dimension
... degenerate submanifolds of R n which are not necessarily analytic. The comple- mentary upper bound associated with (1.5) has been established [6, 29] under additional geometric restrictions on M and a more limited range ... See full document
21
A non-linear structure-preserving matrix method for the computation of the coefficients of an approximate greatest common divisor of two Bernstein polynomials
... One application of the work described in this paper is, as noted above, the calculation of the points of intersection of two curves. Another application is the computation of multiple roots of a polynomial, where the ... See full document
38
New Lower Bounds for the Border Rank of Matrix Multiplication
... to lower bound problems coming from the Geometric Complexity Theory (GCT) introduced by Mulmuley and Sohoni [19], in particular to separate the determinant (small weakly skew circuits) from polynomials with small ... See full document
14
Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation
... In the last decade, Bunea et al. [22–24] addressed the predictive error of the LASSO in a series of papers. There are several results here with different assumptions in this string of papers, one of which matches the ... See full document
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