
Ensemble Riemannian Data Assimilation over the Wasserstein Space
In this paper, we present a new ensemble data assimilation paradigm over...
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Message Passing Least Squares Framework and its Application to Rotation Synchronization
We propose an efficient algorithm for solving group synchronization unde...
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Robust Multiobject Matching via Iterative Reweighting of the Graph Connection Laplacian
We propose an efficient and robust iterative solution to the multiobjec...
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Novelty Detection via Robust Variational Autoencoding
We propose a new method for novelty detection that can tolerate nontrivi...
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Regularized Variational Data Assimilation for Bias Treatment using the Wasserstein Metric
This paper presents a new variational data assimilation (VDA) approach f...
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A Provably Robust Multiple Rotation Averaging Scheme for SO(2)
We give adversarial robustness results for synchronization on the rotati...
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Robust Group Synchronization via CycleEdge Message Passing
We propose a general framework for group synchronization with adversaria...
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Robust Subspace Recovery with Adversarial Outliers
We study the problem of robust subspace recovery (RSR) in the presence o...
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Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
We propose a neural network for unsupervised anomaly detection with a no...
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Algorithms for ℓ_pbased semisupervised learning on graphs
We develop fast algorithms for solving the variational and gametheoreti...
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Solving Jigsaw Puzzles By The Graph Connection Laplacian
We propose a novel mathematical framework to address the problem of auto...
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Graph Generation via Scattering
Generative networks have made it possible to generate meaningful signals...
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Phase transition in random tensors with multiple spikes
Consider a spiked random tensor obtained as a mixture of two components:...
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Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble
We propose a strategy for improving camera location estimation in struct...
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Graph Convolutional Neural Networks via Scattering
We generalize the scattering transform to graphs and consequently constr...
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An Overview of Robust Subspace Recovery
This paper will serve as an introduction to the body of work on robust s...
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Exact Camera Location Recovery by Least Unsquared Deviations
We establish exact recovery for the Least Unsquared Deviations (LUD) alg...
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A WellTempered Landscape for Nonconvex Robust Subspace Recovery
We present a mathematical analysis of a nonconvex energy landscape for ...
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Distributed Robust Subspace Recovery
We study Robust Subspace Recovery (RSR) in distributed settings. We cons...
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Fast Landmark Subspace Clustering
Kernel methods obtain superb performance in terms of accuracy for variou...
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Riemannian MultiManifold Modeling
This paper advocates a novel framework for segmenting a dataset in a Rie...
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Fast, Robust and Nonconvex Subspace Recovery
This work presents a fast and nonconvex algorithm for robust subspace r...
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Better Feature Tracking Through Subspace Constraints
Feature tracking in video is a crucial task in computer vision. Usually,...
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A New Approach To TwoView Motion Segmentation Using Global Dimension Minimization
We present a new approach to rigidbody motion segmentation from two vie...
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Spectral Clustering Based on Local PCA
We propose a spectral clustering method based on local principal compone...
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Robust computation of linear models by convex relaxation
Consider a dataset of vectorvalued observations that consists of noisy ...
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A Novel MEstimator for Robust PCA
We study the basic problem of robust subspace recovery. That is, we assu...
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Robust recovery of multiple subspaces by geometric l_p minimization
We assume i.i.d. data sampled from a mixture distribution with K compone...
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lpRecovery of the Most Significant Subspace among Multiple Subspaces with Outliers
We assume data sampled from a mixture of ddimensional linear subspaces ...
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Hybrid Linear Modeling via Local Bestfit Flats
We present a simple and fast geometric method for modeling data by a uni...
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Randomized hybrid linear modeling by local bestfit flats
The hybrid linear modeling problem is to identify a set of ddimensional...
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Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric lp Minimization
We assume data independently sampled from a mixture distribution on the ...
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Spectral clustering based on local linear approximations
In the context of clustering, we assume a generative model where each cl...
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Median Kflats for hybrid linear modeling with many outliers
We describe the Median KFlats (MKF) algorithm, a simple online method f...
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Foundations of a Multiway Spectral Clustering Framework for Hybrid Linear Modeling
The problem of Hybrid Linear Modeling (HLM) is to model and segment data...
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Gilad Lerman
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Professor of Mathematics at the University of Minnesota, Director of the IMA Data Science Lab, Director of the Minnesota Center for Industrial Mathematics (MCIM)