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Found 14 papers, 6 papers with code
Fast computation of latent correlations
Latent Gaussian copula models provide a powerful means to perform multi-view data integration since these models can seamlessly express dependencies between mixed variable types (binary, continuous, zero-inflated) via latent Gaussian correlations.
Computation Methodology
Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification
Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed.
Non-convex Global Minimization and False Discovery Rate Control for the TREX
The TREX is a recently introduced method for performing sparse high-dimensional regression.
Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring
We consider the two-group classification problem and propose a kernel classifier based on the optimal scoring framework.
Double-matched matrix decomposition for multi-view data
We consider the problem of extracting joint and individual signals from multi-view data, that is data collected from different sources on matched samples.
Sparse quadratic classification rules via linear dimension reduction
We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices.
Oracle Inequalities for High-dimensional Prediction
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic net, and many others.
Structural Learning and Integrative Decomposition of Multi-View Data
We call this model SLIDE for Structural Learning and Integrative DEcomposition of multi-view data.
Penalized versus constrained generalized eigenvalue problems
We investigate the difference between using an $\ell_1$ penalty versus an $\ell_1$ constraint in generalized eigenvalue problems, such as principal component analysis and discriminant analysis.
Simultaneous sparse estimation of canonical vectors in the p>>N setting
Secondly, we propose an extension of this form to the $p\gg N$ setting and achieve feature selection by using a group penalty.
Optimal variable selection in multi-group sparse discriminant analysis
This article considers the problem of multi-group classification in the setting where the number of variables $p$ is larger than the number of observations $n$.
Supervised Classification Using Sparse Fisher's LDA
We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type estimator for the covariance matrix.
Joint association and classification analysis of multi-view data
A distinct advantage of JACA is that it can be applied to the multi-view data with block-missing structure, that is to cases where a subset of views or class labels is missing for some subjects.
Compressing Large Sample Data for Discriminant Analysis
Large-sample data became prevalent as data acquisition became cheaper and easier.
