8 citations · 17 across the 9 of their papers we have counts for
9 papers
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Ou…
Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery with Manifold Learning
Ido Cohen, Dan Valsky, Ronen Talmon
During Deep Brain Stimulation(DBS) surgery for treating Parkinson's disease, one vital task is to detect a specific brain area called the Subthalamic Nucleus(STN) and a sub-territo…
ManiFeSt: Manifold-based Feature Selection for Small Data Sets
David Cohen, Tal Shnitzer, Yuval Kluger +1
In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels captu…
Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation
Or Yair, Almog Lahav, Ronen Talmon
In this paper, we present new results on the Riemannian geometry of symmetric positive semi-definite (SPSD) matrices. First, based on an existing approximation of the geodesic path…
No equations, no parameters, no variables: data, and the reconstruction of normal forms by learning informed observation geometries
Or Yair, Ronen Talmon, Ronald R. Coifman +1
The discovery of physical laws consistent with empirical observations lies at the heart of (applied) science and engineering. These laws typically take the form of nonlinear differ…
Multimodal Latent Variable Analysis
Vardan Papyan, Ronen Talmon
Consider a set of multiple, multimodal sensors capturing a complex system or a physical phenomenon of interest. Our primary goal is to distinguish the underlying sources of variabi…