no code implementations • 29 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.
no code implementations • 22 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.
no code implementations • 9 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.
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.
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.
no code implementations • 8 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.
no code implementations • 20 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.