Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data
复数域稀疏可分因子分析及局部场电位数据应用
Complex-valued arrays arise in signal processing, where scientific interpretation depends on retaining amplitude and phase information. Existing covariance estimation methods either ignore the multiway organization of such data or rely on real-domain embeddings that do not directly exploit their complex structure. We develop sparse separable factor analysis (SSFA), a latent factor model for complex-valued arrays with a separable covariance structure across modes. Each…