4 papers
The Generalized Proximity Forest
Ben Shaw, Adam Rustad, Sofia Pelagalli Maia +2
Recent work has demonstrated the utility of Random Forest (RF) proximities for various supervised machine learning tasks, including outlier detection, missing data imputation, and…
Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
Jake S. Rhodes, Adam G. Rustad, Sofia Pelagalli Maia +5
Missing data is a common problem in time series data. Most methods for imputation ignore label information pertaining to the time series even if that information exists. In this pa…
Guided Manifold Alignment with Geometry-Regularized Twin Autoencoders
Jake S. Rhodes, Adam G. Rustad, Marshall S. Nielsen +3
Manifold alignment (MA) involves a set of techniques for learning shared representations across domains, yet many traditional MA methods are incapable of performing out-of-sample e…
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…