603 citations · 616 across the 4 of their papers we have counts for
4 papers
Spatially orthogonal factor models for spatial transcriptomics and remote sensing data
Dan Cunha, Lukas M. Weber, Mark A. Friedl +1
Principal component analyses are often applied to spatial data towards inference on latent modes of spatial variation. These analyses are widespread across domains including spatia…
Bernstein Polynomial Processes for Continuous Time Change Detection
Dan Cunha, Mark Friedl, Luis Carvalho
There is a lack of methodological results for continuous time change detection due to the challenges of noninformative prior specification and efficient posterior inference in this…
Functional factor analysis for periodic remote sensing data
Chong Liu, Surajit Ray, Giles Hooker +1
We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions d…
Identifying Mislabeled Training Data
C. E. Brodley, M. A. Friedl
This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accu…