
Certainty Equivalent Quadratic Control for Markov Jump Systems
Realworld control applications often involve complex dynamics subject t...
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HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
Principal component analysis (PCA) is a classical and ubiquitous method ...
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Supervised PCA: A Multiobjective Approach
Methods for supervised principal component analysis (SPCA) aim to incorp...
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Preference Modeling with ContextDependent Salient Features
We consider the problem of estimating a ranking on a set of items from n...
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Grassmannian Optimization for Online Tensor Completion and Tracking in the tSVD Algebra
We propose a new streaming algorithm, called TOUCAN, for the tensor comp...
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Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Online Matrix Factorization (OMF) is a fundamental tool for dictionary l...
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Optimally Weighted PCA for HighDimensional Heteroscedastic Data
Modern applications increasingly involve highdimensional and heterogene...
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Streaming PCA and Subspace Tracking: The Missing Data Case
For many modern applications in science and engineering, data are collec...
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Tensor Methods for Nonlinear Matrix Completion
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
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Deep Unsupervised Clustering Using Mixture of Autoencoders
Unsupervised clustering is one of the most fundamental challenges in mac...
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Subspace Clustering using Ensembles of KSubspaces
We present a novel approach to the subspace clustering problem that leve...
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Algebraic Variety Models for HighRank Matrix Completion
We consider a generalization of lowrank matrix completion to the case w...
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RealTime Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning
Though distribution system operators have been adding more sensors to th...
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Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace...
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Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data
Subspace learning and matrix factorization problems have a great many ap...
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Leveraging Union of Subspace Structure to Improve Constrained Clustering
Many clustering problems in computer vision and other contexts are also ...
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On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semiparametric model...
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Matrix Completion Under Monotonic Single Index Models
Most recent results in matrix completion assume that the matrix under co...
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DistancePenalized Active Learning Using Quantile Search
Adaptive sampling theory has shown that, with proper assumptions on the ...
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Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
It has been observed in a variety of contexts that gradient descent meth...
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Online Algorithms for FactorizationBased Structure from Motion
We present a family of online algorithms for realtime factorizationbas...
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On GROUSE and Incremental SVD
GROUSE (Grassmannian RankOne Update Subspace Estimation) is an incremen...
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Iterative Grassmannian Optimization for Robust Image Alignment
Robust highdimensional data processing has witnessed an exciting develo...
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HighRank Matrix Completion and Subspace Clustering with Missing Data
This paper considers the problem of completing a matrix with many missin...
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Online Robust Subspace Tracking from Partial Information
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracki...
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Rank Minimization over Finite Fields: Fundamental Limits and CodingTheoretic Interpretations
This paper establishes informationtheoretic limits in estimating a fini...
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Laura Balzano
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Assistant Professor in Electrical Engineering and Computer Science at the University of Michigan