# Multi-view mid fusion: a universal approach for learning in an HDLSS setting

arXiv: https://arxiv.org/abs/2507.06026 · HTML: https://mlsanity.com/paper/2507.06026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Nonparametric Canonical Correlation Analysis](https://mlsanity.com/paper/1511.04839/) (2015)
- [A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples](https://mlsanity.com/paper/2112.01800/) (2021)
- [The classification for High-dimension low-sample size data](https://mlsanity.com/paper/2006.13018/) (2020)
- [On Generalizations of Some Distance Based Classifiers for HDLSS Data](https://mlsanity.com/paper/1902.03295/) (2019)
- [Distance-weighted Support Vector Machine](https://mlsanity.com/paper/1310.3003/) (2013)
- [Random Forest Kernel for High-Dimension Low Sample Size Classification](https://mlsanity.com/paper/2310.14710/) (2023)

## Abstract (arXiv)

The high-dimensional low-sample-size (HDLSS) setting presents significant challenges in various applications where the feature dimension far exceeds the number of available samples. This paper introduces a universal approach for learning in HDLSS setting using multi-view mid fusion techniques. It shows how existing mid fusion multi-view methods perform well in an HDLSS setting even if no inherent views are provided. Three view construction methods are proposed that split the high-dimensional feature vectors into smaller subsets, each representing a different view. Extensive experimental validation across model-types and learning tasks confirm the effectiveness and generalization of the approach. We believe the work in this paper lays the foundation for further research into the universal benefits of multi-view mid fusion learning.