URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.
Random forests,
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ts-AUC, a new non-parametric multivariate two-sample test, detects posturographic differences between fallers and non-fallers in Parkinsonian syndromes where adjusted univariate tests do not.
Presents new image-based eye gaze tracking algorithms and applies them to biometric identification and activity recognition tasks.
citing papers explorer
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Geodesic Learning via Unsupervised Decision Forests
URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.
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Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning
ts-AUC, a new non-parametric multivariate two-sample test, detects posturographic differences between fallers and non-fallers in Parkinsonian syndromes where adjusted univariate tests do not.
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Image based Eye Gaze Tracking and its Applications
Presents new image-based eye gaze tracking algorithms and applies them to biometric identification and activity recognition tasks.