论文标题
用于缓慢特征分析的量子启发的经典算法
Quantum-Inspired Classical Algorithm for Slow Feature Analysis
论文作者
论文摘要
最近，量子计算的兴趣激增，因为它具有指数加速算法（包括机器学习算法）的能力。但是，Tang建议也可以在经典计算机上进行指数速度。在本文中，我们提出了一种用于缓慢特征分析的算法，该算法是一种机器学习算法，该算法提取了缓慢变化的特征，并具有运行时间O（polylog（n）poly（d））。为了实现这一目标，我们假设对输入数据的必要预处理以及支持特定采样方案的数据结构的存在。算法的分析借用了基质扰动理论的结果，这对于算法的正确性至关重要。这项工作证明了可以使用量子启发的计算的可能应用和程度。
Recently, there has been a surge of interest for quantum computation for its ability to exponentially speed up algorithms, including machine learning algorithms. However, Tang suggested that the exponential speed up can also be done on a classical computer. In this paper, we proposed an algorithm for slow feature analysis, a machine learning algorithm that extracts the slow-varying features, with a run time O(polylog(n)poly(d)). To achieve this, we assumed necessary preprocessing of the input data as well as the existence of a data structure supporting a particular sampling scheme. The analysis of algorithm borrowed results from matrix perturbation theory, which was crucial for the algorithm's correctness. This work demonstrates the possible application and extent for which quantum-inspired computation can be used.
