搜索结果: 1-15 共查到“理论统计学 rank”相关记录24条 . 查询时间(0.031 秒)
Rank 2 Integrable Systems of Prym Varieties
Level integrable system Prym varieties
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2014/12/29
Rank 2 Integrable Systems of Prym Varieties.
A Rank Minrelation - Majrelation Coefficient
RankMajrelationCoefficient
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2013/6/14
Improving the detection of relevant variables using a new bivariate measure could importantly impact variable selection and large network inference methods. In this paper, we propose a new statistical...
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
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2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
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2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
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2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Logarithmic Quantile Estimation for Rank Statistics
Quantile Estimation Rank Statistics
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2013/6/14
We prove an almost sure weak limit theorem for simple linear rank statistics for samples with continuous distributions functions. As a corollary the result extends to samples with ties, and the vector...
A least-squares method for sparse low rank approximation of multivariate functions
least-squares method sparse low rank approximation multivariate functions
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2013/6/14
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regul...
Partial Transfer Entropy on Rank Vectors
Partial Transfer Entropy Rank Vectors
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2013/4/28
For the evaluation of information flow in bivariate time series, information measures have been employed, such as the transfer entropy (TE), the symbolic transfer entropy (STE), defined similarly to T...
Sparse PCA through Low-rank Approximations
Sparse PCA Low-rank Approximations
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2013/4/27
We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of specia...
Selecting the rank of SVD by Maximum Approximation Capacity
Approximation Capacity Selecting the rank of SVD
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2011/3/25
Truncated Singular Value Decomposition (SVD) calculates the closest rank-k approximation of a given input matrix. Selecting the appropriate rank k defines a critical model order choice in most applica...
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction
Low-Rank Matrix Reconstruction
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2011/3/25
We consider the problem of approximately reconstructing a partially-observed, approximately low-rank matrix. This problem has received much attention lately, mostly using the trace-norm as a surrogate...
Model Selection by Loss Rank for Classification and Unsupervised Learning
Classification graphical models loss rank principle model selection
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2010/11/9
Hutter (2007) recently introduced the loss rank principle (LoRP) as a general-purpose principle for model selection. The LoRP enjoys many attractive prop-erties and deserves further investigations. Th...
The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Model selection lasso loss rank principle shrinkage parameter variable se-lection
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2010/11/9
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularizatio...
Inference based on Procrustes means for the 3D shape of tree boles and mildly rank-deficient diffusion tensors
Shape Spaces Extrinsic Mean Mean Location GeneralProcrustes Analysis Weighted Procrustes Mean Strong Law of Large Numbers
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2010/3/10
A method for inference on cylindricity of tree boles based on 3D
Kendall shapes is presented. This method employs Procrustes means
which are highly popular in the shape community. While for extrinsi...
On Low Rank Matrix Approximations with Applications to Synthesis Problem in Compressed Sensing
Low Rank Matrix Approximations Applications Synthesis Problem Compressed Sensing
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2010/3/9
We consider the synthesis problem of Compressed Sensing –given s and an M×n
matrix A, extract from it an m × n submatrix Am, certified to be s-good, with m
as small as possible. Starting from the ve...