搜索结果: 1-13 共查到“统计学 Manifold”相关记录13条 . 查询时间(0.052 秒)
Modules over the small quantum group and semi-infinite flag manifold
Abnormal manifold bundle of semi-infinite flag subclass sheaf quantum
2014/12/25
We develop a theory of perverse sheaves on the semi-infinite flag manifold G((t))/N((t)) · T[[t]], and show that the subcategory of Iwahori-monodromy perverse sheaves is equivalent to the regular bloc...
Statistical tests for group comparison of manifold-valued data
Statistical tests group comparison manifold-valued data
2013/6/13
Motivated by population studies of Diffusion Tensor Imaging, the paper investigates the use of mean-based and dispersion-based permutation tests to define and compute the significance of a statistical...
Bayesian Manifold Regression
Asymptotics Contraction rates Dimensional-ity reduction Gaussian process Manifold learning Nonparametric Bayes Subspace learning
2013/6/13
There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors $D$ is large, one encounters a daunting problem in attempting to...
A Diffusion Process on Riemannian Manifold for Visual Tracking
racking Particle filtering Template update Generative Template Model Riemannian manifolds log-transformed space.
2013/5/2
Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updat...
Discriminative Sparse Coding on Multi-Manifold for Data Representation and Classification
Discriminative Sparse Coding Multi-Manifold for Data Representation Classification
2012/9/18
Sparse coding has been popularly used as an effective data represen-tation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional s...
Manifold embedding for curve registration
Manifold learning Intrinsic statistics Structural statistics Graph-based methods Curve alignment Curve registration Warping Model Functional data
2011/6/20
We focus on the problem of finding a good representative of a
sample of random curves warped from a common pattern f. We first
prove that such a problem can be moved onto a manifold framework.
Then...
Conformal geometry of statistical manifold with application to sequential estimation
Affi ne connections Curved exponential family Hyperboloid dis-tribution Information geometry Projective transformation Riemannian metric Space of constant curvature Totally umbilic von Mises-Fisher distribution
2011/3/22
We present a geometrical method for analyzing sequential estimating procedures. It is based on the design principle of the second-order efficient sequential estimation provided in Okamoto, Amari and T...
Conformal geometry of statistical manifold with application to sequential estimation
Affi ne connections Curved exponential family Hyperboloid dis-tribution Information geometry Projective transformation Riemannian metric Space of constant curvature Totally umbilic von Mises-Fisher distribution
2011/3/23
We present a geometrical method for analyzing sequential estimating procedures. It is based on the design principle of the second-order efficient sequential estimation provided in Okamoto, Amari and T...
Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods'' by M. Girolami and B. Calderhead
Riemann manifold Langevin Hamiltonian Monte Carlo methods
2010/11/8
This technical report is the union of two contributions to the discussion of the Read Paper Riemann manifold Langevin and Hamiltonian Monte Carlo methods (Calderhead and Girolami, 2010), presented in ...
Manifold-Based Signal Recovery and Parameter Estimation from Compressive Measurements
Manifolds dimensionality reduction random projections Compressive Sensing spar-sity signal recovery parameter estimation
2010/3/10
A field known as Compressive Sensing (CS) has recently emerged to help address the growing
challenges of capturing and processing high-dimensional signals and data sets. CS exploits the
surprising f...
Stereological formulas for manifold processes。
A scale-based approach to finding effective dimensionality in manifold learning
Primary manifold learning intrinsic dimension scale space secondary hypothesis test multivariate analysis
2009/9/16
The discovering of low-dimensional manifolds in high-dimensional data is one of the main goals in manifold learning. We propose a new approach to identify the effective dimension (intrinsic dimension)...
We give a generalization in the non-compact case to various positivity theorems obtained by Malliavin Calculus in the compact case.