论文标题
生物启发的随机预测，用于稳定，稀疏分类
Bioinspired random projections for robust, sparse classification
论文作者
论文摘要
受生物传感系统中随机投影的使用的启发，我们提出了一种用于处理分类问题数据的新算法。这是基于对人脑和果蝇的嗅觉系统的观察结果，并涉及将数据随机投射到一个大大增加尺寸的空间中，然后再应用CAP操作以截断较小的条目。这导致一种简单的算法在计算上非常有效，可以用来给出稀疏的表示，分类精度损失最小，或者提供改善的鲁棒性，从某种意义上说，当噪声添加到数据中时，分类精度得到提高。这是通过数值实验证明的，这些实验补充了理论结果，表明所产生的信号转换在适当的意义上是连续且可逆的。
Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and involves randomly projecting data into a space of greatly increased dimension before applying a cap operation to truncate the smaller entries. This leads to a simple algorithm that is very computationally efficient and can be used to either give a sparse representation with minimal loss in classification accuracy or give improved robustness, in the sense that classification accuracy is improved when noise is added to the data. This is demonstrated with numerical experiments, which supplement theoretical results demonstrating that the resulting signal transform is continuous and invertible, in an appropriate sense.
