搜索结果: 1-15 共查到“Principal Components”相关记录20条 . 查询时间(0.078 秒)
Prediction by Supervised Principal Components
Gene expression Microarray Regression Survival analysis
2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
Canonical correlation analysis DNA copy number Integrative genomic analysis L1 Matrix decomposition Principal component analysis Sparse principal component analysis SVD.
2015/8/21
We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as X ˆ = k K=1 dkukvk T , where dk, uk, and vk m...
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of
data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets
of...
ON THE DISTRIBUTION OF THE LARGEST EIGENVALUE IN PRINCIPAL COMPONENTS ANALYSIS
LARGEST EIGENVALUE PRINCIPAL COMPONENTS
2015/8/20
Let x1 denote the square of the largest singular value of an n × p
matrix X, all of whose entries are independent standard Gaussian varates. Equivalently, x1 is the largest principal component vari...
Classification of Multi-spectral, Multi-temporal And Multi-sensor Images Using Principal Components Analysis and Artificial Neural Networks: Beykoz Case
Land Cover Classification Artificial Neural Networks
2015/7/9
The thematic maps derived from remotely-sensed images are invaluable sources of information for various investigations since they
provide spatial and temporal information about the nature of Earth su...
Entropy based determination of optimal principal components of Airborne Prism Experiment (APEX) imaging spectrometer data for improved land cover classification
High resolution data hyper spectral land cover classification feature extraction
2014/12/15
Hyperspectral data finds applications in the domain of remote sensing. However, with the increase in amounts of information and advantages associated, come the "curse" of dimensionality and additional...
Near-Optimal Algorithms for Differentially-Private Principal Components
Near-Optimal Algorithms Differentially-Private Principal Components
2012/9/19
Principal components analysis (PCA) is a standard tool for identifying good low-dimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensiti...
Covariate adjusted functional principal components analysis for longitudinal data
Functional data analysis functional principal componentsanalysis local linear regression longitudinal data analysis smoothing sparse data
2010/3/11
Classical multivariate principal component analysis has been extended
to functional data and termed functional principal component
analysis (FPCA). Most existing FPCA approaches do not accommodate
...
A Monte Carlo comparison between ridge and principal components regression methods
Multicollinearity ridge regression principal component regression
2010/9/15
A basic assumption concerned with general linear regression model is that there is no correlation (or no multicollinearity) between the explanatory variables. When this assumption is not satisfied, th...
Interpretation of Water Quality Data by Principal Components Analysis
Multivariate analysis principal components analysis quality variables river-basin
2009/10/14
A variety of methods are being used to display the information which is concealed in the quality variables observed in a water quality monitoring network. A large portion of these approaches are stati...
Functional principal components analysis via penalized rank one approximation
Functional data analysis penalization regularization singular value decomposition
2009/9/16
Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in ...
Improved performance of fault detection based on selection of the optimal number of principal components
Fault detection Fault SNR Sensitivity The number of PCs
2009/9/9
This paper presents a new method that selecting the number of principal components (PCs) in fault detection based on principal component analysis (PCA). Fault signal-to-noise ratio (SNR) is proposed a...
Factors influencing fluffy layer suspended matter (FLSM) properties in the Odra River - Pomeranian Bay - Arkona Deep System (Baltic Sea) as derived by principal components analysis (PCA), and cluster analysis (CA)
fluffy layer suspended matter Odra River Arkona Deep System principal components analysis cluster analysis
2009/5/18
Factors conditioning formation and properties of suspended matter resting on the sea floor (Fluffy Layer Suspended Matter - FLSM) in the Odra river mouth - Arkona Deep system (southern Baltic Sea) wer...
Spatio-temporal variability and principal components of the particle number size distribution in an urban atmosphere
Spatio-temporal variability particle number size distribution urban atmosphere
2009/5/15
A correct description of fine (diameter <1 μm) and ultrafine (<0.1 μm) aerosol particles in urban areas is of interest for particle exposure assessment but also basic atmospheric research. We examined...