High dimensional binary classification under label shift: phase transition and regularization
DOI10.1007/s43670-023-00071-9arXiv2212.00700MaRDI QIDQ6062484
Jiahui Cheng, Tuo Zhao, Wenjing Liao, Unnamed Author, Minshuo Chen
Publication date: 1 December 2023
Published in: Sampling Theory, Signal Processing, and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.00700
linear discriminant analysisbinary classificationlabel shiftdouble descent phenomenonunderparametrized and overparametrized regime
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear inference, regression (62J99) General considerations in statistical decision theory (62C05)
Cites Work
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- Regularized linear discriminant analysis and its application in microarrays
- Sparse linear discriminant analysis by thresholding for high dimensional data
- Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
- Cost-sensitive boosting for classification of imbalanced data
- Spectral analysis of large dimensional random matrices
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- On the dimension effect of regularized linear discriminant analysis
- Surprises in high-dimensional ridgeless least squares interpolation
- On the consistency properties of linear and quadratic discriminant analyses
- Fisher, Neyman, and the Creation of Classical Statistics
- A direct approach to sparse discriminant analysis in ultra-high dimensions
- A Direct Estimation Approach to Sparse Linear Discriminant Analysis
- Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
- Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis
- Generalized Consistent Error Estimator of Linear Discriminant Analysis
- Two Models of Double Descent for Weak Features
- The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve
- Benign overfitting in linear regression
- A model of double descent for high-dimensional binary linear classification
- A Large Dimensional Study of Regularized Discriminant Analysis
- Reconciling modern machine-learning practice and the classical bias–variance trade-off
- DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
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