# Imputation of missing values in multi-view data

arXiv: https://arxiv.org/abs/2210.14484 · HTML: https://mlsanity.com/paper/2210.14484/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 92 references that are in the map.
- First posted on arXiv 26 Oct 2022.

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

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Multimodal Generative Models for Scalable Weakly-Supervised Learning](https://mlsanity.com/paper/1802.05335/) (2018)
- [Missing Data Imputation with Adversarially-trained Graph Convolutional Networks](https://mlsanity.com/paper/1905.01907/) (2019)
- [On the consistency of supervised learning with missing values](https://mlsanity.com/paper/1902.06931/) (2019)
- [VIGAN: Missing View Imputation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.06724/) (2017)
- [Structured Matrix Completion with Applications to Genomic Data Integration](https://mlsanity.com/paper/1504.01823/) (2015)
- [Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems](https://mlsanity.com/paper/2112.11507/) (2021)
- [Prediction with Missing Data via Bayesian Additive Regression Trees](https://mlsanity.com/paper/1306.0618/) (2013)
- [Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning](https://mlsanity.com/paper/1811.02316/) (2018)
- [Generalized Integrative Principal Component Analysis for Multi-Type Data with Block-Wise Missing Structure](https://mlsanity.com/paper/1808.02193/) (2018)
- [View selection in multi-view stacking: Choosing the meta-learner](https://mlsanity.com/paper/2010.16271/) (2020)
- [Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification](https://mlsanity.com/paper/2108.05761/) (2021)
- [Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute](https://mlsanity.com/paper/1605.02138/) (2016)

## Built on by (most cited first, 1 of 1)

- [Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy](https://mlsanity.com/paper/2605.25050/) (2026)

## Abstract (arXiv)

Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the application of conditional imputation methods computationally infeasible. However, the multi-view structure could be leveraged to reduce the complexity and computational load of imputation. We introduce a new imputation method based on the existing stacked penalized logistic regression (StaPLR) algorithm for multi-view learning. It performs imputation in a dimension-reduced space to address computational challenges inherent to the multi-view context. We compare the performance of the new imputation method with several existing imputation algorithms in simulated data sets and a real data application. The results show that the new imputation method leads to competitive results at a much lower computational cost, and makes the use of advanced imputation algorithms such as missForest and predictive mean matching possible in settings where they would otherwise be computationally infeasible.