Using visual statistical inference to better understand random class separations in high dimension, low sample size data
DOI10.1007/s00180-014-0534-xzbMath1317.65046OpenAlexW1984217368WikidataQ57392168 ScholiaQ57392168MaRDI QIDQ2354730
Niladri Roy Chowdhury, Dianne Cook, Heike Hofmann, Mahbubul Majumder, Eun-Kyung Lee, Amy L. Toth
Publication date: 24 July 2015
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-014-0534-x
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Graphical methods in statistics (62A09)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Distance-Weighted Discrimination
- Boundary behavior in high dimension, low sample size asymptotics of PCA
- Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
- Projection pursuit
- Independent component analysis, a new concept?
- Validation of Visual Statistical Inference, Applied to Linear Models
- Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
- Penalized Classification using Fisher’s Linear Discriminant
- Statistical inference for exploratory data analysis and model diagnostics
- Feature selection by higher criticism thresholding achieves the optimal phase diagram
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- A Projection Pursuit Algorithm for Exploratory Data Analysis
- Geometric Representation of High Dimension, Low Sample Size Data
This page was built for publication: Using visual statistical inference to better understand random class separations in high dimension, low sample size data
