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576
Principal eigenvectors of irregular graphs
- ELA
, 2007
"... Let G be a connected graph. This paper studies the extreme entries of the principal eigenvector x of G, the unique positive unit eigenvector corresponding to the greatest eigenvalue λ1 of the adjacency matrix of G. If G has maximum degree ∆, the greatest entry xmax Õ of x isat most 1 / 1+λ2 1 /∆. ..."
Abstract
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Cited by 5 (2 self)
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Let G be a connected graph. This paper studies the extreme entries of the principal eigenvector x of G, the unique positive unit eigenvector corresponding to the greatest eigenvalue λ1 of the adjacency matrix of G. If G has maximum degree ∆, the greatest entry xmax Õ of x isat most 1 / 1+λ2 1
CONTROLLING SPEECH DISTORTION IN ADAPTIVE FREQUENCY-DOMAIN PRINCIPAL EIGENVECTOR BEAMFORMING
"... Broadband adaptive beamformers, which use a narrowband SNRmaximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methods are investigated to control the speech distortion by comparing the eigenvect ..."
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the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming
Efficient Protocols for Principal Eigenvector Computation over Private Data
, 2011
"... In this paper we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data encrypt ..."
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In this paper we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data
Blind Adaptive Principal Eigenvector Beamforming for Acoustical Source Separation
"... For separating multiple speech signals given a convolutive mixture, time-frequency sparseness of the speech sources can be exploited. In this paper we present a multi-channel source separation method based on the concept of approximate disjoint orthogonality of speech signals. Unlike binary masking ..."
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of singlechannel signals as e.g. applied in the DUET algorithm we use a likelihood mask to control the adaptation of blind principal eigenvector beamformers. Furthermore orthogonal projection of the adapted beamformer filters leads to mutually orthogonal filter coefficients thus enhancing the demixing performance
Decision Aiding Decision-making with the AHP: Why is the principal eigenvector necessary
"... Abstract In this paper it is shown that the principal eigenvector is a necessary representation of the priorities derived from a positive reciprocal pairwise comparison judgment matrix A ¼ ða ij Þ when A is a small perturbation of a consistent matrix. When providing numerical judgments, an individu ..."
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Abstract In this paper it is shown that the principal eigenvector is a necessary representation of the priorities derived from a positive reciprocal pairwise comparison judgment matrix A ¼ ða ij Þ when A is a small perturbation of a consistent matrix. When providing numerical judgments
A LOWER BOUND ON THE ENTRIES OF THE PRINCIPAL EIGENVECTOR OF A GRAPH
"... Abstract. We obtain a lower bound on each entry of the princi-pal eigenvector of a non-regular connected graph. 1. ..."
Abstract
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Cited by 1 (1 self)
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Abstract. We obtain a lower bound on each entry of the princi-pal eigenvector of a non-regular connected graph. 1.
SIMPLE, EFFECTIVE COMPUTATION OF PRINCIPAL EIGEN-VECTORS AND THEIR EIGENVALUES AND APPLICATION TO HIGH-RESOLUTION ESTIMATION OF FREQUENCIES
, 1985
"... under Contracts NOOOi4-83-K-0664 & NOOO14-84-K-0445 ..."
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
Abstract
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Cited by 907 (36 self)
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and the eigenvalue problem. Key words. perturbation theory, random matrix, linear system, least squares, eigenvalue, eigenvector, invariant subspace, singular value AMS(MOS) subject classifications. 15A06, 15A12, 15A18, 15A52, 15A60 1. Introduction. Let A be a matrix and let F be a matrix valued function of A
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
- IEEE Trans. Pattern Anal. Machine Intell
, 2004
"... Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D imagematrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature e ..."
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Cited by 327 (13 self)
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Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D imagematrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature
Results 1 - 10
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576
