2 citations · 4 across the 6 of their papers we have counts for
6 papers
Group COMBSS: Group Selection via Continuous Optimization
Anant Mathur, Sarat Moka, Benoit Liquet +1
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is…
Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
Benoit Liquet, Sarat Moka, Samuel Muller
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually m…
Spatial Autoregressive Model on a Dirichlet Distribution
Teo Nguyen, Sarat Moka, Kerrie Mengersen +1
Compositional data find broad application across diverse fields due to their efficacy in representing proportions or percentages of various components within a whole. Spatial depen…
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
David Black, Jaidev Gill, Andrew Xie +4
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmenta…
The SAMI galaxy survey: predicting kinematic morphology with logistic regression
Sam P. Vaughan, Jesse van de Sande, A. Fraser-McKelvie +10
We use the SAMI galaxy survey to study the the kinematic morphology-density relation: the observation that the fraction of slow rotator galaxies increases towards dense environment…
A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors
David Black, Benoit Liquet, Sadahiro Kaneko +3
Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled detection and classification of tumor regions invisible to the human e…