81 citations · 340 across the 21 of their papers we have counts for
5 papers · 1 filter
Optimal Sparse Linear Auto-Encoders and Sparse PCA
Malik Magdon-Ismail, Christos Boutsidis
Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features. Enforcing sparsity on the principal components can promo…
Spectral Clustering via the Power Method -- Provably
Christos Boutsidis, Alex Gittens, Prabhanjan Kambadur
Spectral clustering is one of the most important algorithms in data mining and machine intelligence; however, its computational complexity limits its application to truly large sca…
Spectral Clustering: An empirical study of Approximation Algorithms and its Application to the Attrition Problem
B. Cung, T. Jin, J. Ramirez +3
Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in diffe…
Random Projections for Linear Support Vector Machines
Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail +1
Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-nor…
Deterministic Feature Selection for -means Clustering
Christos Boutsidis, Malik Magdon-Ismail
We study feature selection for -means clustering. Although the literature contains many methods with good empirical performance, algorithms with provable theoretical behavior ha…