TL;DR本文提出了一种新的方法用于词典学习即稀疏编码的问题,其中,算法能够在噪声张量分解方面解决任意泊松(Poisson)噪声情况,并且本算法同样适用于具有更高的稀疏度,并且基于一个使用和分析半正定规划的Sum of Squares层次结构的新方法。
Abstract
We give a new approach to the dictionary learning (also known as "sparse coding") problem of recovering an unknown $n\times m$ matrix $A$ (for $m \geq n$) from examples of the form \[ y = Ax + e, \] where $x$ is