Pages that link to "Item:Q764487"

The following pages link to Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations (Q764487):

Displaying 50 items.

• Reconstruction of a high-dimensional low-rank matrix (Q276225) (← links)
• Correlation tests for high-dimensional data using extended cross-data-matrix methodology (Q391612) (← links)
• Reconstruction of a low-rank matrix in the presence of Gaussian noise (Q391623) (← links)
• PCA consistency for the power spiked model in high-dimensional settings (Q391897) (← links)
• Asymptotics of hierarchical clustering for growing dimension (Q392113) (← links)
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data (Q741160) (← links)
• Estimation of linear functional of large spectral density matrix and application to Whittle's approach (Q825341) (← links)
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context (Q899373) (← links)
• Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix (Q990890) (← links)
• Inference on high-dimensional mean vectors with fewer observations than the dimension (Q1930608) (← links)
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models (Q2000734) (← links)
• Hypothesis tests for high-dimensional covariance structures (Q2042528) (← links)
• Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings (Q2048123) (← links)
• Geometric classifiers for high-dimensional noisy data (Q2062792) (← links)
• Double data piling leads to perfect classification (Q2074331) (← links)
• Consistency of the objective general index in high-dimensional settings (Q2078579) (← links)
• More about asymptotic properties of some binary classification methods for high dimensional data (Q2080163) (← links)
• Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices (Q2091835) (← links)
• Perturbation theory for cross data matrix-based PCA (Q2140856) (← links)
• Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices (Q2196219) (← links)
• Two-stage dimension reduction for noisy high-dimensional images and application to cryogenic electron microscopy (Q2218179) (← links)
• On asymptotic normality of cross data matrix-based PCA in high dimension low sample size (Q2293385) (← links)
• Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model (Q2317309) (← links)
• Location-invariant tests of homogeneity of large-dimensional covariance matrices (Q2321809) (← links)
• Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model (Q2329874) (← links)
• Using visual statistical inference to better understand random class separations in high dimension, low sample size data (Q2354730) (← links)
• Semiparametric estimation of the high-dimensional elliptical distribution (Q2692920) (← links)
• Overview of object oriented data analysis (Q2922172) (← links)
• Two-Stage Procedures for High-Dimensional Data (Q3106536) (← links)
• Authors' Response (Q3106538) (← links)
• Analysis of high-dimensional one group repeated measures designs (Q3462154) (← links)
• Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data with<i>D</i>-Asymptotics (Q3585254) (← links)
• Statistical inference for high-dimension, low-sample-size data (Q4568290) (← links)
• A High-Dimensional Two-Sample Test for Non-Gaussian Data under a Strongly Spiked Eigenvalue Model (Q4578226) (← links)
• A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise (Q4632469) (← links)
• A survey of high dimension low sample size asymptotics (Q4639812) (← links)
• (Q5004041) (← links)
• (Q5011468) (← links)
• A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context (Q5077372) (← links)
• Binary discrimination methods for high-dimensional data with a geometric representation (Q5160208) (← links)
• On Estimation of the Noise Variance in High Dimensional Probabilistic Principal Component Analysis (Q5378155) (← links)
• Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata (Q5894438) (← links)
• Robust PCA for high‐dimensional data based on characteristic transformation (Q6075186) (← links)
• CORRELATION MATRIX OF EQUI-CORRELATED NORMAL POPULATION: FLUCTUATION OF THE LARGEST EIGENVALUE, SCALING OF THE BULK EIGENVALUES, AND STOCK MARKET (Q6095475) (← links)
• High-dimensional hypothesis testing for allometric extension model (Q6113076) (← links)
• Polynomial whitening for high-dimensional data (Q6178887) (← links)
• Effective methodologies for high-dimensional data (Q6486990) (← links)
• Statistical inference under the strongly spiked eigenvalue model (Q6601514) (← links)
• Equality tests of covariance matrices under a low-dimensional factor structure (Q6667486) (← links)
• Test for high-dimensional outliers with principal component analysis (Q6670084) (← links)