110 citations · 225 across the 7 of their papers we have counts for
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cs.LG2014★ 77 cited
A statistical model for tensor PCA
Andrea Montanari, Emile Richard
We consider the Principal Component Analysis problem for large tensors of arbitrary order under a single-spike (or rank-one plus noise) model. On the one hand, we use informati…
stat.ML2014★ 23 cited
Tight convex relaxations for sparse matrix factorization
Emile Richard, Guillaume Obozinski, Jean-Philippe Vert
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and…
cs.IT2014★ 10 cited
Non-negative Principal Component Analysis: Message Passing Algorithms and Sharp Asymptotics
Andrea Montanari, Emile Richard
Principal component analysis (PCA) aims at estimating the direction of maximal variability of a high-dimensional dataset. A natural question is: does this task become easier, and e…