Results 1  10
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131,596
JACOBILIKE NONNEGATIVE JOINT DIAGONALIZATION BY CONGRUENCE
, 2013
"... Jacobilike nonnegative joint diagonalization by congruence ..."
Perturbation of Joint Diagonalizers
, 1995
"... This report gives the first order perturbation of the joint diegonedize tion of e set of commuting matrices. ..."
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Cited by 17 (1 self)
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This report gives the first order perturbation of the joint diegonedize tion of e set of commuting matrices.
On Using Exact Joint Diagonalization for Noniterative Approximate Joint Diagonalization
"... Abstract—We propose a novel, noniterative approach for the problem of nonunitary, leastsquares (LS) approximate joint diagonalization (AJD) of several Hermitian target matrices. Dwelling on the fact that exact joint diagonalization (EJD) of two Hermitian matrices can almost always be easily obtaine ..."
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Cited by 3 (1 self)
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Abstract—We propose a novel, noniterative approach for the problem of nonunitary, leastsquares (LS) approximate joint diagonalization (AJD) of several Hermitian target matrices. Dwelling on the fact that exact joint diagonalization (EJD) of two Hermitian matrices can almost always be easily
CRITICAL POINT ANALYSIS OF JOINT DIAGONALIZATION CRITERIA
"... The stability and sensitivity of joint diagonalization criteria are analyzed using the Hessian of the criteria at their critical points. The sensitivity of some known joint diagonalization criteria is shown to be weak when they are applied to the matrices with closely placed eigenvalues. In such a s ..."
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Cited by 4 (0 self)
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The stability and sensitivity of joint diagonalization criteria are analyzed using the Hessian of the criteria at their critical points. The sensitivity of some known joint diagonalization criteria is shown to be weak when they are applied to the matrices with closely placed eigenvalues. In such a
WHAT CAN MAKE JOINT DIAGONALIZATION DIFFICULT?
"... We show that the issues of uniqueness and noise sensitivity in the problem of matrix joint diagonalization are closely related. We address other factors important in noise sensitivity. We distinguish between orthogonal and nonorthogonal joint diagonalization and argue that the latter can be more di ..."
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Cited by 4 (1 self)
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We show that the issues of uniqueness and noise sensitivity in the problem of matrix joint diagonalization are closely related. We address other factors important in noise sensitivity. We distinguish between orthogonal and nonorthogonal joint diagonalization and argue that the latter can be more
Joint Diagonalization Of Correlation Matrices By Using Newton
 in Proc. SAM, Rosslyn
, 2002
"... This paper addresses the blind signal separation problem in the presence of sensor noise for the case where the source signals are nonstationary and / or nonwhite. This problem can be formulated as a jointdiagonalization problem where the objective is to jointly diagonalize a set of correlation m ..."
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This paper addresses the blind signal separation problem in the presence of sensor noise for the case where the source signals are nonstationary and / or nonwhite. This problem can be formulated as a jointdiagonalization problem where the objective is to jointly diagonalize a set of correlation
Sensitivity analysis for the problem of matrix joint diagonalization,” to appear at
 SIAM journal on matrix analysis and applications (Special Issue on Tensorial Methods
, 2008
"... Abstract. We investigate the sensitivity of the problem of NonOrthogonal (matrix) Joint Diagonalization (NOJD). First, we consider the uniqueness conditions for the problem of Exact Joint Diagonalization (EJD), which is closely related to the issue of uniqueness in tensor decompositions. As a bypr ..."
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Cited by 13 (3 self)
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Abstract. We investigate the sensitivity of the problem of NonOrthogonal (matrix) Joint Diagonalization (NOJD). First, we consider the uniqueness conditions for the problem of Exact Joint Diagonalization (EJD), which is closely related to the issue of uniqueness in tensor decompositions. As a by
Joint Diagonalization via Subspace Fitting Techniques
 In Proc. ICASSP
, 2001
"... INTRODUCTION Suppose that we are given K complex Hermitian matrices Y k of the form where the k are diagonal and real, and E k represents additive noise. The joint diagonalization problem we consider is, given the Y k , to estimate the common factor A. We assume that all Y k are square d d matr ..."
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Cited by 33 (2 self)
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INTRODUCTION Suppose that we are given K complex Hermitian matrices Y k of the form where the k are diagonal and real, and E k represents additive noise. The joint diagonalization problem we consider is, given the Y k , to estimate the common factor A. We assume that all Y k are square d d
A linear leastsquares algorithm for joint diagonalization
 In Proc. 4th Intern. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003
"... We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear leastsquares optimization. The ..."
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Cited by 21 (5 self)
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We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear leastsquares optimization
GRADIENT BASED APPROXIMATE JOINT DIAGONALIZATION BY ORTHOGONAL TRANSFORMS
 16TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO'08), LAUSANNE, AUGUST~2529 2008
, 2008
"... Approximate Joint Diagonalization (AJD) of a set of symmetric matrices by an orthogonal transform is a popular problem in Blind Source Separation (BSS). In this paper we propose a gradient based algorithm which maximizes the sum of squares of diagonal entries of all the transformed symmetric matrice ..."
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Cited by 1 (1 self)
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Approximate Joint Diagonalization (AJD) of a set of symmetric matrices by an orthogonal transform is a popular problem in Blind Source Separation (BSS). In this paper we propose a gradient based algorithm which maximizes the sum of squares of diagonal entries of all the transformed symmetric
Results 1  10
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131,596