Search Results for author: Megasthenis Asteris

Found 7 papers, 0 papers with code

A simple and provable algorithm for sparse diagonal CCA

no code implementations29 May 2016 Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated.

Trading-off variance and complexity in stochastic gradient descent

no code implementations22 Mar 2016 Vatsal Shah, Megasthenis Asteris, Anastasios Kyrillidis, Sujay Sanghavi

Stochastic gradient descent is the method of choice for large-scale machine learning problems, by virtue of its light complexity per iteration.

Bipartite Correlation Clustering -- Maximizing Agreements

no code implementations9 Mar 2016 Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros G. Dimakis

We present a novel approximation algorithm for $k$-BCC, a variant of BCC with an upper bound $k$ on the number of clusters.

Clustering

Orthogonal NMF through Subspace Exploration

no code implementations NeurIPS 2015 Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis

Our algorithm relies on a novel approximation to the related Nonnegative Principal Component Analysis (NNPCA) problem; given an arbitrary data matrix, NNPCA seeks $k$ nonnegative components that jointly capture most of the variance.

Clustering

Sparse PCA via Bipartite Matchings

no code implementations NeurIPS 2015 Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis

We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance.

Stay on path: PCA along graph paths

no code implementations8 Jun 2015 Megasthenis Asteris, Anastasios Kyrillidis, Alexandros G. Dimakis, Han-Gyol Yi and, Bharath Chandrasekaran

We introduce a variant of (sparse) PCA in which the set of feasible support sets is determined by a graph.

The Sparse Principal Component of a Constant-rank Matrix

no code implementations20 Dec 2013 Megasthenis Asteris, Dimitris S. Papailiopoulos, George N. Karystinos

In this work, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable.

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