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FIR Filter Design via Spectral Factorization and Convex Optimization
FIR Filter Design Spectral Factorization Convex Optimization
2015/7/13
We consider the design of finite impulse response (FIR) filters subject to upper and lower bounds on the frequency response magnitude. The associated optimization problems, with the filter coefficient...
The spectral curve of a quaternionic holomorphic line bundle over a 2-torus
Riemann surface the spectral curve curve four yuan dirichlet energy
2014/12/24
A conformal immersion of a 2-torus into the 4-sphere is characterized by an auxiliary Riemann surface, its spectral curve. This complex curve encodes the monodromies of a certain Dirac type operator o...
Discrete holomorphic geometry I. Darboux transformations and spectral curves
Discrete regular concept map discrete Riemann discrete differential geometry the discrete surface
2014/12/24
Finding appropriate notions of discrete holomorphic maps and, more generally, conformal immersions of discrete Riemann surfaces into 3-space is an important problem of discrete differential geometry a...
CLT for linear spectral statistics of random matrix $S^{-1}T$
CLT linear spectral statistics random matrix $S^{-1}T$
2013/6/13
This paper proposes a CLT for linear spectral statistics of random matrix $S^{-1}T$ for a general non-negative definite and {\bf non-random} Hermitian matrix $T$.
A Note on Central Limit Theorems for Linear Spectral Statistics of Large Dimensional F-matrix
Linear spectral statistics central limit theorem centralized sample covari-ance matrix centralizedF-matrix simplified sample covariance matrix simplified F-matrix
2013/6/13
Sample covariance matrix and multivariate $F$-matrix play important roles in multivariate statistical analysis. The central limit theorems {\sl (CLT)} of linear spectral statistics associated with the...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Epidemic diffusion on a graph is a dynamic process that transitions simultaneously to all of a node's neighbors, in contrast to a random walk, which selects only a single neighbor. Epidemic diffusion ...
Spectral Risk Measures, With Adaptions For Stochastic Optimization
Spectral Risk Measures Adaptions Stochastic Optimization
2012/11/22
Stochastic optimization problems often involve the expectation in its objective. When risk is incorporated in the problem description as well, then risk measures have to be involved in addition to qua...
A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation
Edge preserving smoother Semiparametric mixture model Partition of unity MISE Variational optimization Thin Plate Splines Spectral embedding Local template mod-els Multiple hypothesis testing.
2012/11/22
This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for loca...
Spline Smoothing for Estimation of Circular Probability Distributions via Spectral Isomorphism and its Spatial Adaptation
Non-parametric density estimation circular data Smoothing Spline empirical Fourier coeffcients Fourier Basis Detection of Localisation Edge preserving function estima-tion
2012/11/22
Consider the problem when $X_1,X_2,..., X_n$ are distributed on a circle following an unknown distribution $F$ on $S^1$. In this article we have consider the absolute general set-up where the density ...
A spectral construction of the extremal t process
elliptical distribution extremalt process max-stable process spectral construction
2012/9/19
The extremaltprocess was proposed in the literature for modelling spatial extremes within a copula framework based on the extreme value limit of ellipticalt distribu-tions (Davison, Padoan and Ribatet...
Distances and Riemannian metrics for multivariate spectral densities
multivariate spectral densities Riemannian metrics
2011/7/19
We first introduce a class of divergence measures between power spectral density matrices. These are derived by comparing the suitability of different models in the context of optimal prediction.
Spectral Methods for Learning Multivariate Latent Tree Structure
Spectral Methods Learning Multivariate Latent Tree Structure
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Spectral Methods for Learning Multivariate Latent Tree Structure
Multivariate Latent Spectral Methods
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/22
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...