Kernel principal component analysis (KPCA) is a powerful tool for nonlinear feature extraction, but its standard formulation is not well-suited for streaming data. Although there are efficient online KPCA solutions, there is a gap in the literature regarding genuinely sparse online KPCA algorithms. This paper introduces a novel, fast, and accurate online fixed-point algorithm designed for sparse kernel principal component extraction. Utilizing a two-level sparsifying strategy, the proposed algorithm efficiently handles streaming data and large datasets within minimal computing and memory requirements, achieving both higher accuracy and sparser components compared to existing online KPCA methods.

中文翻译：

---

稀疏定点在线 KPCA 提取算法


核主成分分析 （KPCA） 是非线性特征提取的强大工具，但其标准公式不太适合流数据。尽管有高效的在线 KPCA 解决方案，但关于真正稀疏的在线 KPCA 算法的文献存在空白。该文介绍了一种新颖、快速、准确的稀疏核主成分提取在线定点算法。利用两级稀疏化策略，所提出的算法以最小的计算和内存要求有效地处理流数据和大型数据集，与现有的在线 KPCA 方法相比，实现了更高的准确性和更稀疏的组件。