
Statistical Learning for Best Practices in Tattoo Removal
The causes behind complications in laserassisted tattoo removal are cur...
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Analysis of Legal Documents via Nonnegative Matrix Factorization Methods
The California Innocence Project (CIP), a clinical law school program ai...
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Modewise Tensor Decompositions: Multidimensional Generalizations of CUR Decompositions
Low rank tensor approximation is a fundamental tool in modern machine le...
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Robust CUR Decomposition: Theory and Imaging Applications
This paper considers the use of Robust PCA in a CUR decomposition framew...
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Applications of Online Nonnegative Matrix Factorization to Image and TimeSeries Data
Online nonnegative matrix factorization (ONMF) is a matrix factorization...
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On a Guided Nonnegative Matrix Factorization
Fully unsupervised topic models have found fantastic success in document...
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Semisupervised NMF Models for Topic Modeling in Learning Tasks
We propose several new models for semisupervised nonnegative matrix fac...
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On Nonnegative Matrix and Tensor Decompositions for COVID19 Twitter Dynamics
We analyze Twitter data relating to the COVID19 pandemic using dynamic ...
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Quantilebased Iterative Methods for Corrupted Systems of Linear Equations
Often in applications ranging from medical imaging and sensor networks t...
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Online nonnegative tensor factorization and CPdictionary learning for Markovian data
Nonnegative Matrix Factorization (NMF) algorithms are fundamental tools ...
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COVID19 Literature TopicBased Search via Hierarchical NMF
A dataset of COVID19related scientific literature is compiled, combini...
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Feature Selection on Lyme Disease Patient Survey Data
Lyme disease is a rapidly growing illness that remains poorly understood...
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Random Vector Functional Link Networks for Function Approximation on Manifolds
The learning speed of feedforward neural networks is notoriously slow a...
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COVID19 Timeseries Prediction by Joint Dictionary Learning and Online NMF
Predicting the spread and containment of COVID19 is a challenge of utmo...
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Tensor Completion through Total Variationwith Initialization from Weighted HOSVD
In our paper, we have studied the tensor completion problem when the sam...
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HOSVDBased Algorithm for Weighted Tensor Completion
Matrix completion, the problem of completing missing entries in a data m...
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Randomized Kaczmarz with Averaging
The randomized Kaczmarz (RK) method is an iterative method for approxima...
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An Iterative Method for Structured Matrix Completion
The task of fillingin or predicting missing entries of a matrix, from a...
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On LargeScale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
There is currently an unprecedented demand for largescale temporal data...
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Topicaware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
We propose a novel model for a topicaware chatbot by combining the trad...
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Sketching for Motzkin's Iterative Method for Linear Systems
Projectionbased iterative methods for solving large overdetermined lin...
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Weighted matrix completion from nonrandom, nonuniform sampling patterns
We study the matrix completion problem when the observation pattern is d...
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Stochastic Iterative Hard Thresholding for LowTuckerRank Tensor Recovery
Lowrank tensor recovery problems have been widely studied in many appli...
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Adaptive SketchandProject Methods for Solving Linear Systems
We present new adaptive sampling rules for the sketchandproject method...
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Iterative Hard Thresholding for Low CPrank Tensor Models
Recovery of lowrank matrices from a small number of linear measurements...
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Bias of Homotopic Gradient Descent for the Hinge Loss
Gradient descent is a simple and widely used optimization method for mac...
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On Inferences from Completed Data
Matrix completion has become an extremely important technique as data sc...
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Datadriven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing
Machine learning algorithms typically rely on optimization subroutines a...
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Matrix Completion With Selective Sampling
Matrix completion is a classical problem in data science wherein one att...
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Lattices from tight frames and vertex transitive graphs
We show that real tight frames that generate lattices must be rational, ...
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An iterative method for classification of binary data
In today's data driven world, storing, processing, and gleaning insights...
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Hierarchical Classification using Binary Data
In classification problems, especially those that categorize data into a...
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An Approximate Message Passing Framework for Side Information
Approximate message passing (AMP) methods have gained recent traction in...
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Analysis of Fast Structured Dictionary Learning
Sparsitybased models and techniques have been exploited in many signal ...
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Randomized Projection Methods for Linear Systems with Arbitrarily Large Sparse Corruptions
In applications like medical imaging, error correction, and sensor netwo...
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Randomized Projection Methods for Corrupted Linear Systems
In applications like medical imaging, error correction, and sensor netwo...
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Analysis of Fast Alternating Minimization for Structured Dictionary Learning
Methods exploiting sparsity have been popular in imaging and signal proc...
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Compressed Anomaly Detection with Multiple Mixed Observations
We consider a collection of independent random variables that are identi...
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Matrix Completion for Structured Observations
The need to predict or fillin missing data, often referred to as matrix...
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Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors
While single measurement vector (SMV) models have been widely studied in...
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Stochastic Greedy Algorithms For Multiple Measurement Vectors
Sparse representation of a single measurement vector (SMV) has been expl...
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Simple Classification using Binary Data
Binary, or onebit, representations of data arise naturally in many appl...
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Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and th...
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Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
We obtain an improved finitesample guarantee on the linear convergence ...
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Nearoptimal compressed sensing guarantees for total variation minimization
Consider the problem of reconstructing a multidimensional signal from an...
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Deanna Needell
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Graduate Student at University of California, Davis, PhD in Mathematics University of California, Davis