1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Random Forest-Supervised Manifold Alignment
Jake S. Rhodes, Adam G. Rustad
Manifold alignment is a type of data fusion technique that creates a shared low-dimensional representation of data collected from multiple domains, enabling cross-domain learning a…
stat.ML2024★ 1 cited
Graph Integration for Diffusion-Based Manifold Alignment
Jake S. Rhodes, Adam G. Rustad
Data from individual observations can originate from various sources or modalities but are often intrinsically linked. Multimodal data integration can enrich information content co…
cs.LG2024
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Shuang Ni, Adrien Aumon, Guy Wolf +2
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods emb…