Finding structured elements from a geometric object, with emphasis on objects containing the origin and hence associated scale ambiguity. (Update: Jan 25 2020)
[S] indicates my contribution.
Finding Sparse Vectors in Linear Subspaces
- Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications (2020)
- Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms (2018)
- Speeding up sum-of-squares for tensor decomposition and planted sparse vectors (2015)
- Dual Principal Component Pursuit (2015)
- Complete Dictionary Recovery over the Sphere (2015)
- Finding a sparse vector in a subspace: Linear sparsity using alternating directions ([S], 2014)
- Exact Recovery of Sparsely-Used Dictionaries (2012)
- Blind Source Separation by Sparse Decomposition in a Signal Dictionary (2001)
Finding Low-rank Matrices in Matrix Subspaces
- Finding a low-rank basis in a matrix subspace (2015)
- Rank-one solutions for homogeneous linear matrix equations over the positive semidefinite cone (2013)
- A simple prior-free method for non-rigid structure-from-motion factorization (2012)
Finding Sparse Elements in Tensor Subspaces
- When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity (2013)
Disclaimer - This page is meant to serve a hub for references on this problem, and does not represent in any way personal endorsement of papers listed here. So I do not hold any responsibility for quality and technical correctness of each paper listed here. The reader is advised to use this resource with discretion.
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