Pages that link to "Item:Q5313460"

The following pages link to Geometric Representation of High Dimension, Low Sample Size Data (Q5313460):

Displaying 50 items.

• Geometric classifiers for high-dimensional noisy data (Q2062792) (← links)
• Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data (Q2068931) (← links)
• Double data piling leads to perfect classification (Q2074331) (← links)
• More about asymptotic properties of some binary classification methods for high dimensional data (Q2080163) (← links)
• Analysis of distance matrices (Q2105395) (← links)
• High-dimensional tests for mean vector: approaches without estimating the mean vector directly (Q2115215) (← links)
• Manifold valued data analysis of samples of networks, with applications in corpus linguistics (Q2135359) (← links)
• New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification (Q2136627) (← links)
• Some clustering-based exact distribution-free \(k\)-sample tests applicable to high dimension, low sample size data (Q2140846) (← links)
• Perturbation theory for cross data matrix-based PCA (Q2140856) (← links)
• High dimension low sample size asymptotics of robust PCA (Q2259533) (← links)
• On stochastic generation of ultrametrics in high-dimensional Euclidean spaces (Q2263137) (← links)
• Treelets -- an adaptive multi-scale basis for sparse unordered data (Q2271330) (← links)
• Discussion of: Treelets -- an adaptive multi-scale basis for sparse unordered data (Q2271331) (← links)
• Data science, big data and statistics (Q2273155) (← links)
• On asymptotic normality of cross data matrix-based PCA in high dimension low sample size (Q2293385) (← links)
• On some graph-based two-sample tests for high dimension, low sample size data (Q2303669) (← links)
• Using visual statistical inference to better understand random class separations in high dimension, low sample size data (Q2354730) (← links)
• Optimal properties of centroid-based classifiers for very high-dimensional data (Q2380097) (← links)
• Some high-dimensional one-sample tests based on functions of interpoint distances (Q2404413) (← links)
• Support vector machine and its bias correction in high-dimension, low-sample-size settings (Q2411297) (← links)
• High dimensional two-sample test based on the inter-point distance (Q2418056) (← links)
• An algorithm for deciding the number of clusters and validation using simulated data with application to exploring crop population structure (Q2441832) (← links)
• A high dimensional dissimilarity measure (Q2674497) (← links)
• On the eigenstructure of covariance matrices with divergent spikes (Q2692533) (← links)
• Change-Point Detection of the Mean Vector with Fewer Observations than the Dimension Using Instantaneous Normal Random Projections (Q2833372) (← links)
• Overview of object oriented data analysis (Q2922172) (← links)
• Unit canonical correlations and high-dimensional discriminant analysis (Q3070630) (← links)
• Counting faces of randomly projected polytopes when the projection radically lowers dimension (Q3079190) (← links)
• Two-Stage Procedures for High-Dimensional Data (Q3106536) (← links)
• Rank tests in heteroscedastic multi-way HANOVA (Q3391784) (← links)
• Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data with<i>D</i>-Asymptotics (Q3585254) (← links)
• Theoretical Measures of Relative Performance of Classifiers for High Dimensional Data with Small Sample Sizes (Q3631449) (← links)
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context (Q3644996) (← links)
• Can we trust the bootstrap in high-dimension? (Q4558141) (← 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)
• Another Look at Distance-Weighted Discrimination (Q4603806) (← links)
• Sure Independence Screening for Ultrahigh Dimensional Feature Space (Q4632602) (← links)
• A survey of high dimension low sample size asymptotics (Q4639812) (← links)
• Two-Step Hypothesis Testing When the Number of Variables Exceeds the Sample Size (Q4921619) (← links)
• Significance test of clustering under high dimensional setting with applications to cancer data (Q4960768) (← links)
• On the limits of clustering in high dimensions via cost functions (Q4969748) (← links)
• A survey on unsupervised outlier detection in high‐dimensional numerical data (Q4969851) (← links)
• (Q5011468) (← links)
• Nonparametric tests for detection of high dimensional outliers (Q5030945) (← links)
• Distance-based outlier detection for high dimension, low sample size data (Q5036480) (← links)
• (Q5054602) (← links)
• Support vector machine and optimal parameter selection for high-dimensional imbalanced data (Q5055167) (← links)
• Bayesian Distance Weighted Discrimination (Q5057252) (← links)