Data dynamics and Low-rank Matrix Approximation
LLORMA: Local Low-Rank Matrix Approximation
... probabilistic matrix factorization (PMF) and later Salakhutdinov and Mnih (2008b) extended matrix factorization to fully Bayesian ap- ...in matrix completion were demonstrated in Toh and Yun (2010); ...
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Lower bounds for the low rank matrix approximation
... a low-rank matrix approximation ...the low-rank matrix approximation ...the low-rank approximation, when a perturbation E exists, there is an ...
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Low-rank matrix approximation with weights or missing data is NP-hard
... When data is missing, the rank-one matrix approximation problem is NP-hard in ...the matrix, and affected by a relatively low level of noise, the original uncorrupted ...
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Complex data analytics via sparse, low-rank matrix approximation
... and data samples from about 4000 to 19, ...and data samples from 500 to ...LIBSVM Data 4 ...this data for a two-cluster partition: the real and the ...
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Practical Sketching Algorithms for Low-Rank Matrix Approximation
... FOR LOW-RANK MATRIX APPROXIMATION ∗ JOEL ...constructing low-rank approximations of an input matrix from a random linear image, or sketch, of the ...input matrix, ...
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Randomized Single-View Algorithms for Low-Rank Matrix Approximation
... FOR LOW-RANK MATRIX APPROXIMATION JOEL ...constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a ...input ...
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Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation
... the low-rank matrix model, which we surmise is due to the spring of new theory, new applications are being quickly discovered that deviate from the matrix completion setup (such as quantum ...
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Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
... STREAMING LOW-RANK MATRIX APPROXIMATION WITH AN APPLICATION TO SCIENTIFIC SIMULATION \ast JOEL ...of data matrices that arise from large-scale scientific simulations and data ...
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A non-linear structure preserving matrix method for the low rank approximation of the Sylvester resultant matrix
... resultant matrix, the entries of which are functions of the coefficients of the polynomials, is singular if and only if the curves ...inexact data may imply they do not intersect, in which case the design ...
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Sparse Principal Component Analysis via Regularized Low Rank Matrix Approximation
... the data matrix and extract the PCs through solving a low rank matrix approxi- mation ...the data covariance matrix is ...multivariate data and ...
Sparse principal component analysis via regularized low rank matrix approximation
... for data analysis and dimension reduction in applications throughout science and ...the data matrix and extract the PCs through solving a low rank matrix approximation ...
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Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
... synthetic data to demonstrate that our method dominates existing techniques [18, 37] for fixed-rank psd ...Psd matrix approximation is a ubiquitous problem, so we expect these results to have ...
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Low-Rank Approximation and Difusion Maps
... square matrix whose dimension is the number of ...a low-rank approximation for Diffusion Maps using Nystr¨ om’s ...the matrix, which is especially critical if the applications have ...
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Algorithms for ℓp Low Rank Approximation
... given matrix by a low-rank matrix so as to mini- mize the entry-wise ` p -approximation error, for any p ≥ 1; the case p = 2 is the classical SVD ...of low- rank ...
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Low rank matrix completion
... Figure 2-3 is the completion percentage plot of this local algorithm with rank 2 input data matrices of size 100 x 100 under different sampling densities and var[r] ...
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Algorithms and literate programs for weighted low-rank approximation with missing data
... 3.1 Alternating projections The first solution method is motivated by the fact that (7) is linear separately in either P or L. Indeed, by fixing either P or L in (7) the minimization over the free parameter is a ...
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Structured low-rank approximation and its applications
... Keywords: Low-rank approximation; Total least squares; System identification; Errors-in-variables modeling; Behaviors ...to data can be achieved, both concep- tually and algorithmically, by ...
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Deterministic algorithms for the low rank approximation of matrices
... Low rank approximations using the symmetric eigenvalue decomposition Objectives and key idea Formulation of the matrix eigenvalue problem Standard algorithm for the Hermitian eigendecomp[r] ...
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Channel Estimation in Massive Multi-User MIMO Systems Based on Low-Rank Matrix Approximation
... The main contribution of chapter 5 is to develop a new channel estimation scheme capable of mitigating pilot contamination problem in a multi-cell TDD massive MU-MIMO system. Hence, the applications of the NN and IWNN ...
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Weighted Low-Rank Approximation of Matrices and Background Modeling
... Weighted Low-Rank Approximation of Matrices and Background Modeling Aritra Dutta, Xin Li, and Peter Richt´arik Abstract—We primarily study a special a weighted low-rank ...
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