搜索结果: 1-12 共查到“principal component”相关记录12条 . 查询时间(0.108 秒)
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently,...
Principal Component Pursuit with Reduced Linear Measurements
Principal Component Pursuit Reduced Linear Measurements low-rank matrix sparse matrix
2012/3/1
In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data pr...
Principal Component Analysis with Contaminated Data：The High Dimensional Case
Statistical Learning Dimension Reduction Principal Component Analysis Robustness Outlier
2010/3/10
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for
contaminated data in the high dimensional regime, where the number of observations is of the s...
A note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions
distance between subspaces influential observations perturbation principal component analysis
2009/9/16
In this paper we introduce an influence measure based on second order expansion of the RV and GCD measures for the comparison between unperturbed and perturbed eigenvectors of a symmetric matrix estim...
Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Principal Component Analysis
Multiblock kernel methods Nonlinear Component Analysis Process Monitoring Fault detection
2009/9/9
In this paper, a multiblock kernel principal component analysis (MBKPCA) algorithm is proposed. Then a new fault detection and diagnosis approach based on MBKPCA is proposed to monitor large-scale pro...
Theoretical Justification of Decision Rules for the Number of Factors: Principal Component Analysis as a Substitute for Factor Analysis in One-Factor Cases
cubic equation greater-than-one rule number of factors principal component analysis representation of a polynomial in terms of a remainder sequence scree test
2009/3/5
Applying principal component analysis as a substitute for factor analysis, we often adopt the following greater-than-one rule to decide the number of factors, k: Take the number of eigenvalues of the ...
Finite sample approximation results for principal component analysis：a matrix perturbation approach
Principal component analysis spiked covariance model randommatrix theory matrix perturbation phase transition
2010/3/17
Principal component analysis (PCA) is a standard tool for dimensional
reduction of a set of n observations (samples), each with
p variables. In this paper, using a matrix perturbation approach, we
...
Unified Principal Component Analysis with Generalized Covariance Matrix for Face Recognition
Unified Principal Component Analysis Covariance Matrix Face Recognition
2010/12/20
Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these methods, and a novel framew...
Decomposable Principal Component Analysis
Principal Component Analysis Gaussian graphical models
2010/4/30
We consider principal component analysis (PCA) in decomposable Gaussian graphical models. We
exploit the prior information in these models in order to distribute its computation. For this purpose,we ...
Unified Principal Component Analysis with Generalized Covariance Matrix for Face Recognition
Unified Principal Component Analysis Generalized Covariance Matrix Face Recognition
2013/7/17
Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these methods, and a novel framew...
Projection-Pursuit Based Principal Component Analysis: a Large Sample Theory
Dispersion matrices eigenvalues and eigenvectors empirical processes principal component analysis projection pursuit (PP)
2007/12/10
摘要 The principal component analysis (PCA) is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit (PP), a more general class of dimension...
Local functional principal component analysis
Local functional principal component analysis random functions Covariance operators
2010/4/26
Covariance operators of random functions are crucial tools to study
the way random elements concentrate over their support. The principal
component analysis of a random function X is well-known from...