1 citations · 1 across the 10 of their papers we have counts for
11 papers
Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control
Nathan Mankovich, Andrei Gavrilov, Gustau Camps-Valls
We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system…
Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
Nathan Mankovich, Kai-Hendrik Cohrs, Homer Durand +3
Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevant societal, economic, and enviro…
A Flag Decomposition for Hierarchical Datasets
Nathan Mankovich, Ignacio Santamaria, Gustau Camps-Valls +1
Flag manifolds encode nested sequences of subspaces and serve as powerful structures for various computer vision and machine learning applications. Despite their utility in tasks s…
Optimal codes in the Stiefel manifold
John Jasper, Nathan Mankovich, Dustin G. Mixon
We consider the coding problem in the Stiefel manifold with chordal distance. After considering various low-dimensional instances of this problem, we use Rankin's bounds on spheric…
Recovering Latent Confounders from High-dimensional Proxy Variables
Nathan Mankovich, Homer Durand, Emiliano Diaz +2
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, a…
Out-of-distribution robustness for multivariate analysis via causal regularisation
Homer Durand, Gherardo Varando, Nathan Mankovich +1
We propose a regularisation strategy of classical machine learning algorithms rooted in causality that ensures robustness against distribution shifts. Building upon the anchor regr…
Fun with Flags: Robust Principal Directions via Flag Manifolds
Nathan Mankovich, Gustau Camps-Valls, Tolga Birdal
Principal component analysis (PCA), along with its extensions to manifolds and outlier contaminated data, have been indispensable in computer vision and machine learning. In this w…
Featurizing Koopman Mode Decomposition For Robust Forecasting
David Aristoff, Jeremy Copperman, Nathan Mankovich +1
This article introduces an advanced Koopman mode decomposition (KMD) technique -- coined Featurized Koopman Mode Decomposition (FKMD) -- that uses delay embedding and a learned Mah…
Chordal Averaging on Flag Manifolds and Its Applications
Nathan Mankovich, Tolga Birdal
This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median of a set of points on a flag manifold under the chordal metric. The flag manifo…
The Flag Median and FlagIRLS
Nathan Mankovich, Emily King, Chris Peterson +1
Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust represent…
Randomized Extended Kaczmarz is a Limit Point of Sketch-and-Project
Benjamin Jarman, Nathan Mankovich, Jacob D. Moorman
The sketch-and-project (SAP) framework for solving systems of linear equations has unified the theory behind popular projective iterative methods such as randomized Kaczmarz, rando…