Now showing items 11-14 of 14
Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization
Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and ...
Solution-Recovery in L1-norm for Non-square Linear Systems: Deterministic Conditions and Open Questions
Consider an over-determined linear system A'x = b and an under-determined linear system By = c. Given b = A'x* + h, under what conditions x* will minimize the residual A'x - b in L1-norm? On the other hand, given c = Bh, ...
A Sparse, Bound-Respecting Parametrization of Velocity Models
We present a parsimonious representation of velocity models which allows for user-defined placement of nodes. Mild restrictions are imposed on the data structure so that a computationally efficient algorithm can be used ...
A Simple Proof for Recoverability of L1-Minimization: Go Over or Under?
It is well-known by now that L1 minimization can help recover sparse solutions to under-determined linear equations or sparsely corrupted solutions to over-determined equations, and the two problems are equivalent under ...