# Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction

arXiv: https://arxiv.org/abs/2304.04692 · HTML: https://mlsanity.com/paper/2304.04692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application](https://mlsanity.com/paper/1309.1541/) (2013)
- [Deep Generalized Canonical Correlation Analysis](https://mlsanity.com/paper/1702.02519/) (2017)
- [Randomized Nonlinear Component Analysis](https://mlsanity.com/paper/1402.0119/) (2014)
- [Deep Feature Selection using a Teacher-Student Network](https://mlsanity.com/paper/1903.07045/) (2019)
- [sJIVE: Supervised Joint and Individual Variation Explained](https://mlsanity.com/paper/2102.13278/) (2021)
- [Sparse Linear Discriminant Analysis for Multi-view Structured Data](https://mlsanity.com/paper/1911.05643/) (2019)
- [Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data](https://mlsanity.com/paper/1611.01066/) (2016)
- [Bayesian Integrative Analysis and Prediction with Application to Atherosclerosis Cardiovascular Disease](https://mlsanity.com/paper/2005.11586/) (2020)
- [Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking -- An Application to COVID-19 severity](https://mlsanity.com/paper/2111.09964/) (2021)
- [Large-scale Nonlinear Variable Selection via Kernel Random Features](https://mlsanity.com/paper/1804.07169/) (2018)

## Built on by (most cited first, 2 of 2)

- [mvlearnR and Shiny App for multiview learning](https://mlsanity.com/paper/2311.16181/) (2023)
- [A deep learning pipeline for cross-sectional and longitudinal multiview data integration](https://mlsanity.com/paper/2312.01238/) (2023)

## Abstract (arXiv)

We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or more classes. The proposed methods model nonlinear relationships in multiview data together with predicting a clinical outcome and are capable of identifying variables or groups of variables that best contribute to the relationships among the views. We use the idea that random Fourier bases can approximate shift-invariant kernel functions to construct nonlinear mappings of each view and we use these mappings and the outcome variable to learn view-independent low-dimensional representations. Through simulation studies, we show that the proposed methods outperform several other linear and nonlinear methods for multiview data integration. When the proposed methods were applied to gene expression, metabolomics, proteomics, and lipidomics data pertaining to COVID-19, we identified several molecular signatures forCOVID-19 status and severity. Results from our real data application and simulations with small sample sizes suggest that the proposed methods may be useful for small sample size problems. Availability: Our algorithms are implemented in Pytorch and interfaced in R and would be made available at: https://github.com/lasandrall/RandMVLearn.