Apr 26, 2015 · This recovery problem is central to the theoretical understanding of dictionary learning, which seeks a sparse representation for a collection ...

Nov 11, 2015 · This recovery problem is central to theoretical understanding of dictionary learning, which seeks a sparse representation for a collection of ...

Nov 23, 2016 · This recovery problem is central to theoretical understanding of dictionary learning, which seeks a sparse representation for a collection of ...

Aug 26, 2020 · Abstract—We consider the problem of recovering a complete. (i.e., square and invertible) matrix A0, from Y ∈ Rn× p with.

This recovery setting is central to the theoretical understanding of dictionary learning. We give the first efficient algorithm that provably recovers A 0 when ...

Dictionary learning (DL) is the problem of finding a sparse representation for a collection of input signals. Its applications span.

Apr 26, 2015 · Abstract. We consider the problem of recovering a complete (i.e., square and invertible) matrix A0, from. Y ∈ Rn×p with Y = A0X0, ...

This work gives the first efficient algorithm that provably recovers A0 when X0 has O (n) nonzeros per column, under suitable probability model for X0, ...

Motivation: Dictionary Learning. Given Y , find (A,X) such that Y ≈ AX, with X as sparse as possible. Very successful in classical image processing, visual.

Apr 26, 2015 · Abstract. We consider the problem of recovering a complete (i.e., square and invertible) matrix A0, from. Y ∈ Rn×p with Y = A0X0, ...