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Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets c...
Real-Variable Theory and Fourier Integral Operators on Semisimple Lie Groups and Symmetric Spaces of Real Rank One
Real-Variable Theory Fourier Integral Operators Semisimple Lie Groups Symmetric Spaces Real Rank One
2014/4/3
Let G be a non-compact connected semisimple Lie group of real rank one with finite center, K a maximal compact subgroup of G and X = G/K an associated symmetric space of real rank one. We will p...
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
Low-rank matrix recovery or completion Robust principal component analysis Nuclear norm minimization
2010/12/13
This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted. This probl...
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
spectral clustering affinity matrix learning rank minimization robust estimation
2010/12/16
Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group)1 highdimensional structural data such as those (approximately) lying on subspaces2 or low-dime...