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中山大学岭南学院高级计量经济学课件（II：Panel Data）CH3 Covariance Structure and Robust Covariance Estimation
中山大学岭南学院 高级计量经济学 课件（II：Panel Data） CH3 Covariance Structure and Robust Covariance Estimation
2017/6/14
中山大学岭南学院高级计量经济学课件（II：Panel Data）CH3 Covariance Structure and Robust Covariance Estimation。
SINGLE-AND TWO-CHANNEL NOISE REDUCTION FOR ROBUST SPEECH RECOGNITION IN CAR
automatic speech recognition robustness car noise two-channel noise reduction
2015/9/29
Hands-free operation of a mobile phone in car raises major challenges for acoustic enhancement algorithms and speech recognition engines. This is due to a degradation of the speech signal caused by re...
For an equilibrium of a general dynamical system, the
domain of stability of a linear feedback controller is enlarged by the use
of a general "hybrid" or "switching" strategy. The strategy is illust...
Robust solutions to l1, l2, and l_infinity uncertain linear approximation problems using convex optimization
Linear random and uncertain data convex optimization
2015/8/11
We present minimax and stochastic formulations of some linear approximation problems with uncertain data in R^n equipped with the Euclidean (l2), Absolute-sum (l1) or Chebyshev (l-infinity) norms. We ...
In Chebyshev finite-impulse response (FIR) equalization, we design an FIR filter that minimizes the Chebyshev equalization error, i.e., the maximum absolute deviation between the equalized and the des...
Robust efficient frontier analysis with a separable uncertainty model
The mean - variance model effective portfolio construction model parameters return on assets uncertainty analysis and numerical examples
2015/8/10
Mean-variance (MV) analysis is often sensitive to model mis-specification or uncertainty, meaning that the MV efficient portfolios constructed with an estimate of the model parameters (i.e., the expec...
Cutting-set methods for robust convex optimization with pessimizing oracles
robust optimization cutting-set methods semi-infi nite programming minimax optimization games
2015/8/10
We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values. We describe a...
Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information
Random matrices free probability sparsity trigonometric expansions uncertainty principle convex optimization duality in optimization total-variation minimization image reconstruction linear programming
2015/6/17
This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f ∈ CN and a randomly chosen set of frequencies Ω. Is it pos...
Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
Uncertainty principle applications of uncertainty principles random matrices eigenvalues of random matrices sparsity trigonometric expansion convex optimization duality in optimization basis pursuit wavelets linear programming
2015/6/17
In this paper, we develop a robust uncertainty principle for finite signals in CN which states that for nearly all choices T,Ω ⊂ {0, . . . , N − 1} such that |T| + |Ω| (log N...
Highly Robust Error Correction by Convex Programming
Linear codes decoding of (random) linear codes sparse solutions to underdetermined systems `1 minimization linear programming second-order cone programming the Dantzig selector restricted orthonormality Gaussian random matrices and random projections
2015/6/17
This paper discusses a stylized communications problem where one wishes to transmit a real-valued signal x ∈ Rn (a block of n pieces of information) to a remote receiver. We ask whether it is possible...
Robust Principal Component Analysis?
Principal components robustness vis-a-vis outliers nuclear-norm minimization `1-norm minimization duality low-rank matrices sparsity video surveillance
2015/6/17
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove...
Robust Subspace Clustering
Subspace clustering spectral clustering LASSO Dantzig selector `1 minimization multiple hypothesis testing true and false discoveries geometric functional analysis nonasymptotic random matrix theory
2015/6/17
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired...
Optimal Compound Orthogonal Arrays and Single Arrays for Robust Parameter Design Experiments
Compound Orthogonal Arrays Single Arrays Robust Parameter Design Experiments
2015/3/20
Optimal Compound Orthogonal Arrays and Single Arrays for Robust Parameter Design Experiments.
Mathematics of the total alkalinity–pH equation – pathway to robust and universal solution algorithms: the SolveSAPHE package v1.0.1
the total alkalinity–pH equation pathway to robust
2014/12/12
The total alkalinity–pH equation, which relates total alkalinity and pH for a given set of total concentrations
of the acid–base systems that contribute to total alkalinity
in a given water sample, ...
A Mo dified Schur Metho d for Robust Pole Assignment in State Feedback Control
pole assignment state feedback control robustness departure from normality real Schur form
2014/10/10
Recently, a SCHUR metho d was prop osed in [8] to solve the robust p ole assignment problem in state feedback control. It takes the departure from normality of the closed-lo op system matrix A c as th...