We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts.

通过研究一种在尖峰维沙特模型下的主成分分析问题，我们揭示了信号中的结构通过一类子空间模型予以捕捉。在统计和计算统一的视角下，我们建立了依赖于问题实例几何结构的基本限制，并展示了一种自然的投影幂法在解的统计可接近最优邻域上表现出的局部收敛性。我们通过分析两个重要特例，即基于路径和树稀疏性的初始方法和匹配证据的计算难度，来补充这些结果。总体而言，我们的结果表明，多个针对普通稀疏主成分分析观察到的现象自然地扩展到其结构化对应物中。

稀疏主成分分析的算法和障碍是否适用于其他结构化情境？