
Approximation Algorithms for Sparse Principal Component Analysis
We present three provably accurate, polynomial time, approximation algor...
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Speeding up Linear Programming using Randomized Linear Algebra
Linear programming (LP) is an extremely useful tool and has been success...
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Randomized Iterative Algorithms for Fisher Discriminant Analysis
Fisher discriminant analysis (FDA) is a widely used method for classific...
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Constructing Compact Brain Connectomes for Individual Fingerprinting
Recent neuroimaging studies have shown that functional connectomes are u...
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Numerical Linear Algebra in the Sliding Window Model
We initiate the study of numerical linear algebra in the sliding window ...
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Randomized Linear Algebra Approaches to Estimate the Von Neumann Entropy of Density Matrices
The von Neumann entropy, named after John von Neumann, is the extension ...
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Lectures on Randomized Numerical Linear Algebra
This chapter is based on lectures on Randomized Numerical Linear Algebra...
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Coreset Construction via Randomized Matrix Multiplication
Coresets are small sets of points that approximate the properties of a l...
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A Randomized Rounding Algorithm for Sparse PCA
We present and analyze a simple, twostep algorithm to approximate the o...
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Feature Selection for Ridge Regression with Provable Guarantees
We introduce singleset spectral sparsification as a deterministic sampl...
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Approximating Sparse PCA from Incomplete Data
We study how well one can recover sparse principal components of a data ...
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Recovering PCA from Hybrid(ℓ_1,ℓ_2) Sparse Sampling of Data Elements
This paper addresses how well we can recover a data matrix when only giv...
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Random Projections for Linear Support Vector Machines
Let X be a data matrix of rank ρ, whose rows represent n points in ddim...
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The Fast Cauchy Transform and Faster Robust Linear Regression
We provide fast algorithms for overconstrained ℓ_p regression and relate...
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Random Projections for kmeans Clustering
This paper discusses the topic of dimensionality reduction for kmeans c...
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Petros Drineas
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