# A Novel Random Forest Dissimilarity Measure for Multi-View Learning

arXiv: https://arxiv.org/abs/2007.02572 · HTML: https://mlsanity.com/paper/2007.02572/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark](https://mlsanity.com/paper/2406.01607/) (2024)
- [Forest Proximities for Time Series](https://mlsanity.com/paper/2410.03098/) (2024)
- [Random Forest Kernel for High-Dimension Low Sample Size Classification](https://mlsanity.com/paper/2310.14710/) (2023)
- [Revisiting Forest Proximities via Sparse Leaf-Incidence Kernels](https://mlsanity.com/paper/2601.02735/) (2026)
- [Multi-view user representation learning for user matching without personal information](https://mlsanity.com/paper/2312.14533/) (2023)
- [Holistic analysis on the sustainability of Federated Learning across AI product lifecycle](https://mlsanity.com/paper/2312.14628/) (2023)

## Abstract (arXiv)

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.