Spectral Factorization via a Linear Matrix Inequality
Spectral decomposition method of dialog state tracking via collective matrix factorization
... collective matrix factorization task that constitutes the learning procedure of the state tracking ...decomposed matrix to the form of latent variables {A, B, C}, also called ...the matrix ...
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Robust control of uncertain multi-inventory systems via Linear Matrix Inequality
... time linear multi–inventory sys- tem with unknown demands bounded within ellipsoids and controls bounded within ...saturated linear state feedback ...
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Robust control of uncertain multi-inventory systems via Linear Matrix Inequality
... 6 Conclusions and future works We have addressed the problem of ε-stabilizing the inventory of a continuous time lin- ear multi–inventory system with unknown demands bounded within ellipsoids and controls bounded within ...
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An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization
... Optimization methods used for (non-Bayesian) NMF are much faster than the MCMC methods used for Bayesian NMF though: the original multiplica- tive algorithm Lee and Seung ( 1999 , 2001 ), projected gradient descent ( ...
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Matrix Completion via Nonconvex Factorization: Algorithms and Theory
... a matrix sensing type problem (Pauli measurements), not the case with missing ...to matrix completion in Gross [4] and Recht [5], which improved the sample complexity bound to ˜ O(nr log 2 n) with much ...
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FIR Filter Design via Semidenite Programming and Spectral Factorization
... Stanford University, Stanford, CA 94305 clive@isl.stanford.edu, boyd@isl.stanford.edu, vandenbe@isl.stanford.edu Abstract We present a new semide nite programming approach to FIR lter design with arbitrary upper and ...
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Application of a Displacement Structure for Acceleration of Novel Matrix Spectral Factorization Algorithm
... algebraic linear equations using the underlying displacement structure of the coefficients matrix of the ...novel matrix spectral factorization ...
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Learning Cross lingual Word Embeddings via Matrix Co factorization
... languages via matrix factorization, with in- duced constraints to assure cross-lingual seman- tic ...simple linear projections, are automatically ...
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Fast, Sparse Matrix Factorization and Matrix Algebra via Random Sampling for Integral Equation Formulations in Electromagnetics
... system matrix is only one component of the challenge. The resulting linear systems are often very difficult to ...LU factorization or any other fundamental factorization to Z in order to ...
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Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization
... term-document matrix as input in the context of text clustering, sparse–dense matrix multiplication (SpMM) was the main computational bottleneck for computing the ...adjacency matrix with z nonzeros ...
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Tensor completion via a multi-linear low-n-rank factorization model
... 5.1. Numerical simulations The tested low-n-rank tensors are created randomly by the following procedure. The N-way Tensor Toolbox [24] is used to generate a third order tensor with the size of I 1 I 2 I 3 and the ...
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Multi-task sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising
... of spectral correlation and spatial correlation information into account, a few band images with high noise level can be well recovered via using the related clean signal in other ...the spectral ...
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Tensor Factorization via Matrix Factorization
... TFMF = random projections + simultaneous diagonalization + plugin estimates Works for orthogonal, non-orthogonal, symmetric, asymetric, high order tensors Is more accurate that state-of-[r] ...
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SPECTRAL FACTORIZATION
... Spectral factorization is a method of finding the one time function which is also minimum phase.. The minimum-phase function has many uses.[r] ...
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Nonnegative Matrix Factorization via Rank-one Downdate
... Nonnegative matrix factorization (NMF) was popularized as a tool for data mining by Lee and Seung in ...a matrix with nonnegative entries by a product of two low-rank matrices, also with nonnegative ...
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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition
... multichannel spectral factorization algorithm which can be utilized to cal- culate the approximate spectral factor of any para-Hermitian polynomial ...polynomial matrix eigenvalue ...
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Area Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
... traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix ...iterative spectral unmixing ...
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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition
... multichannel spectral factorization algorithm which can be utilized to cal- culate the approximate spectral factor of any para-Hermitian polynomial ...polynomial matrix eigenvalue ...
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Advances in Linear Matrix InequalityAdvances in Linear Matrix Inequality Methods in Control Methods in Control
... This chapter proposes a parametric multiplier approach to deriving parametric Lya- punov functions for robust stability analysis of linear systems involving uncertain pa- rameters.. This[r] ...
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DETERMINANT MAXIMIZATION WITH LINEAR MATRIX INEQUALITY CONSTRAINTS
... indicates that the method we describe solves the max-det problem (1.1) in a number of iterations that hardly varies with problem size, and typically ranges between 5 and 50; each iteration involves solving a system of ...
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