Search Results for author: Irina Gaynanova

Found 13 papers, 5 papers with code

Double-matched matrix decomposition for multi-view data

1 code implementation7 May 2021 Dongbang Yuan, Irina Gaynanova

We consider the problem of extracting joint and individual signals from multi-view data, that is data collected from different sources on matched samples.

Fast computation of latent correlations

3 code implementations24 Jun 2020 Grace Yoon, Christian L. Müller, Irina Gaynanova

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

Compressing Large Sample Data for Discriminant Analysis

no code implementations8 May 2020 Alexander F. Lapanowski, Irina Gaynanova

Large-sample data became prevalent as data acquisition became cheaper and easier.

Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring

1 code implementation12 Feb 2019 Alexander F. Lapanowski, Irina Gaynanova

We consider the two-group classification problem and propose a kernel classifier based on the optimal scoring framework.

Classification Feature Selection +1

Joint association and classification analysis of multi-view data

no code implementations20 Nov 2018 Yunfeng Zhang, Irina Gaynanova

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.

Classification General Classification

Sparse quadratic classification rules via linear dimension reduction

1 code implementation13 Nov 2017 Irina Gaynanova, Tianying Wang

We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices.

Classification Dimensionality Reduction +2

Structural Learning and Integrative Decomposition of Multi-View Data

no code implementations20 Jul 2017 Irina Gaynanova, Gen Li

We call this model SLIDE for Structural Learning and Integrative DEcomposition of multi-view data.

Dimensionality Reduction

Oracle Inequalities for High-dimensional Prediction

no code implementations1 Aug 2016 Johannes Lederer, Lu Yu, Irina Gaynanova

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.

Non-convex Global Minimization and False Discovery Rate Control for the TREX

1 code implementation22 Apr 2016 Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian Müller

The TREX is a recently introduced method for performing sparse high-dimensional regression.

Optimal variable selection in multi-group sparse discriminant analysis

no code implementations23 Nov 2014 Irina Gaynanova, Mladen Kolar

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 $n$.

Variable Selection

Penalized versus constrained generalized eigenvalue problems

no code implementations22 Oct 2014 Irina Gaynanova, James Booth, Martin T. Wells

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.

Variable Selection

Simultaneous sparse estimation of canonical vectors in the p>>N setting

no code implementations24 Mar 2014 Irina Gaynanova, James G. Booth, Martin T. Wells

Secondly, we propose an extension of this form to the $p\gg N$ setting and achieve feature selection by using a group penalty.

Classification Consistency Feature Selection +1

Supervised Classification Using Sparse Fisher's LDA

no code implementations21 Jan 2013 Irina Gaynanova, James G. Booth, Martin T. Wells

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.

Classification General Classification +1

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