170 citations · 527 across the 39 of their papers we have counts for
6 papers · 1 filter
Column Selection via Adaptive Sampling
Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm tha…
A Randomized Rounding Algorithm for Sparse PCA
Kimon Fountoulakis, Abhisek Kundu, Eugenia-Maria Kontopoulou +1
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. Our approach first solves a L1 penalized version of the NP-hard s…
Feature Selection for Ridge Regression with Provable Guarantees
Saurabh Paul, Petros Drineas
We introduce single-set spectral sparsification as a deterministic sampling based feature selection technique for regularized least squares classification, which is the classificat…
Approximating Sparse PCA from Incomplete Data
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail
We study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization probl…
Recovering PCA from Hybrid- Sparse Sampling of Data Elements
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail
This paper addresses how well we can recover a data matrix when only given a few of its elements. We present a randomized algorithm that element-wise sparsifies the data, retaining…
A Randomized Algorithm for Approximating the Log Determinant of a Symmetric Positive Definite Matrix
Christos Boutsidis, Petros Drineas, Prabhanjan Kambadur +2
We introduce a novel algorithm for approximating the logarithm of the determinant of a symmetric positive definite (SPD) matrix. The algorithm is randomized and approximates the tr…