Pages that link to "Item:Q5313460"

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

Displaying 50 items.

• On some transformations of high dimension, low sample size data for nearest neighbor classification (Q255361) (← links)
• ``Virus hunting'' using radial distance weighted discrimination (Q262396) (← links)
• The remarkable simplicity of very high dimensional data: application of model-based clustering (Q263091) (← links)
• On high-dimensional sign tests (Q282562) (← links)
• Significance analysis of high-dimensional, low-sample size partially labeled data (Q286481) (← links)
• Identification of consistent functional genetic modules (Q306654) (← links)
• Sparse \(p\)-adic data coding for computationally efficient and effective big data analytics (Q344041) (← links)
• Impacts of high dimensionality in finite samples (Q385798) (← links)
• Correlation tests for high-dimensional data using extended cross-data-matrix methodology (Q391612) (← links)
• PCA consistency for the power spiked model in high-dimensional settings (Q391897) (← links)
• A nonparametric two-sample test applicable to high dimensional data (Q391926) (← links)
• Asymptotics of hierarchical clustering for growing dimension (Q392113) (← links)
• PCA and eigen-inference for a spiked covariance model with largest eigenvalues of same asymptotic order (Q398204) (← links)
• Regularized orthogonal linear discriminant analysis (Q411935) (← links)
• On the border of extreme and mild spiked models in the HDLSS framework (Q413760) (← links)
• Boundary behavior in high dimension, low sample size asymptotics of PCA (Q432317) (← links)
• Indexability, concentration, and VC theory (Q450514) (← links)
• Mixed modeling with whole genome data (Q454774) (← links)
• Convergence and prediction of principal component scores in high-dimensional settings (Q620562) (← links)
• On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators (Q681518) (← links)
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data (Q741160) (← links)
• On high dimensional two-sample tests based on nearest neighbors (Q746878) (← links)
• Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations (Q764487) (← links)
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context (Q899373) (← links)
• A note on testing complete independence for high dimensional data (Q900534) (← links)
• Gene selection and prediction for cancer classification using support vector machines with a reject option (Q901576) (← links)
• A new and fast implementation for null space based linear discriminant analysis (Q962681) (← links)
• Adjusted support vector machines based on a new loss function (Q970172) (← links)
• Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix (Q990890) (← links)
• Robust centroid based classification with minimum error rates for high dimension, low sample size data (Q1021991) (← links)
• Outlier identification in high dimensions (Q1023500) (← links)
• PCA consistency in high dimension, low sample size context (Q1043724) (← links)
• Symmetry in data mining and analysis: a unifying view based on hierarchy (Q1048428) (← links)
• Model-based clustering of high-dimensional data: a review (Q1621282) (← links)
• Multivariate location and scatter matrix estimation under cellwise and casewise contamination (Q1654233) (← links)
• Testing independence in high dimensions using Kendall's tau (Q1662048) (← links)
• Continuum directions for supervised dimension reduction (Q1662921) (← links)
• Distribution-free high-dimensional two-sample tests based on discriminating hyperplanes (Q1694021) (← links)
• On the ultrametric generated by random distribution of points in Euclidean spaces of large dimensions with correlated coordinates (Q1695088) (← links)
• Fast, linear time, \(m\)-adic hierarchical clustering for search and retrieval using the Baire metric, with linkages to generalized ultrametrics, hashing, formal concept analysis, and precision of data measurement (Q1760308) (← links)
• Inference on high-dimensional mean vectors with fewer observations than the dimension (Q1930608) (← links)
• Projection pursuit via white noise matrices (Q1936426) (← links)
• Regularized \(k\)-means clustering of high-dimensional data and its asymptotic consistency (Q1950809) (← links)
• Distance-based and RKHS-based dependence metrics in high dimension (Q1996774) (← links)
• Robust multivariate nonparametric tests via projection averaging (Q1996775) (← links)
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models (Q2000734) (← links)
• Subspace rotations for high-dimensional outlier detection (Q2022542) (← links)
• Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings (Q2023463) (← links)
• Interpoint distance based two sample tests in high dimension (Q2040058) (← links)
• Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings (Q2048123) (← links)