4 citations · 16 across the 27 of their papers we have counts for
4 papers · 1 filter
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam S. Charles
Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and in…
Co-manifold learning with missing data
Gal Mishne, Eric C. Chi, Ronald R. Coifman
Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the ro…
Data-Driven Tree Transforms and Metrics
Gal Mishne, Ronen Talmon, Israel Cohen +2
We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that…
Diffusion Nets
Gal Mishne, Uri Shaham, Alexander Cloninger +1
Non-linear manifold learning enables high-dimensional data analysis, but requires out-of-sample-extension methods to process new data points. In this paper, we propose a manifold l…