# Interpretable Deep Learning Methods for Multiview Learning

arXiv: https://arxiv.org/abs/2302.07930 · HTML: https://mlsanity.com/paper/2302.07930/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [A Variational Information Bottleneck Approach to Multi-Omics Data Integration](https://mlsanity.com/paper/2102.03014/) (2021)
- [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)
- [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)
- [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)
- [Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN](https://mlsanity.com/paper/2302.02759/) (2023)
- [BDgraph: An R Package for Bayesian Structure Learning in Graphical Models](https://mlsanity.com/paper/1501.05108/) (2015)
- [Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information](https://mlsanity.com/paper/1606.02690/) (2016)

## Built on by (most cited first, 5 of 5)

- [From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research](https://mlsanity.com/paper/2507.09028/) (2025)
- [MoT: Modularization-of-Thought Prompting for Effective Code Generation](https://mlsanity.com/paper/2503.12483/) (2025)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [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)

Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) for learning nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and two real-world data, including breast cancer-related gene expression and methylation data. iDeepViewLearn had competitive classification results and identified genes and CpG sites that differentiated between individuals who died from breast cancer and those who did not. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning.