稀疏编码的经典文章翻译

Efficient sparse coding algorithms

ABSTRCT :Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data. However, finding sparse codes remains a very
difficult computational problem. In this paper, we present efficient sparse coding algorithms that are based on iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2 -constrained least squares problem. We propose
novel algorithms to solve both of these optimization problems. Our algorithms result in a significant speed up for sparse coding, allowing us to learn larger sparse codes than possible with previously described algorithms. We apply these algorithms to natural
images and demonstrate that the inferred sparse codes exhibit end-stopping and non-classical receptive field surround suppression and, therefore, may provide a partial explanation for these two phenomena in V1 neurons.

摘要：稀疏编码是一种为激励（类似于神经元刺激响应，译者注）提供简单表示的算法，给定一组无标签的输入数据，它可以找出能够捕获数据中高维特征的基本方法。然而，稀疏编码的寻找过程是非常耗时的。本文提出一种基于递归解决凸包问题的高效稀疏编码算法：L1-正则化的最小二乘问题和L2-约束化的最小二乘问题。我们提出新的算法来解决这两个问题。实验结果表明相对于之前的算法，我们的算法在速度上有了更高的提升，从而可以学习更大的稀疏编码。我们将本文算法应用到真实图像中，实验结果也证明了推导出的稀疏编码具有端点抑制和非经典感受野抑制（这个不明白，译者注），因此，可以提供在V1神经元中存在的以上两个现象的局部解释。