High-Dimensional Block Diagonal Covariance Structure Detection Using Singular Vectors
本文提出一种利用奇异向量的稀疏近似来检测高维数据中块对角协方差结构的方法，无需估计协方差矩阵，通过模拟和真实数据验证了性能。
The assumption of independent subvectors arises in many aspects of multivariate analysis.In most realworld applications, however, we lack prior knowledge about the number of subvectors and the specific variables within each subvector.Yet, testing all these combinations is not feasible.For example, for a data matrix containing 15 variables, there are already 1,382,958,545 possible combinations.Given that zero correlation is a necessary condition for independence, independent subvectors exhibit a block diagonal covariance matrix.This article focuses on the detection of such block diagonal covariance structures in highdimensional data and therefore also identifies uncorrelated subvectors.Our approach exploits the fact that the structure of the covariance matrix is mirrored by the structure of its eigenvectors.However, the true block diagonal structure is masked by noise in the sample case.To address this problem, we propose to use sparse approximations of the sample eigenvectors to reveal the sparse structure of the population eigenvectors.Notably, the right singular vectors of a data matrix with an overall mean of zero are identical to the sample eigenvectors of its covariance matrix.Using sparse approximations of these singular vectors instead of the eigenvectors makes the estimation of the covariance matrix obsolete.We demonstrate the performance of our method through simulations and provide real data examples.