Regularization is a well-known and used statistical approach covering individual points or limit approximations. In this study, the canonical correlation analysis (CCA) process of the paths is discussed with partial least squares (PLS) as the other boundary covering transformation to a symmetric eigenvalue (or singular value) problem dependent on a parameter. Two regularizations of the original criterion in the parameterization domain are compared, i.e. using projection and by identity matrix. We discuss the existence and uniqueness of the analytic path for eigenvalues and corresponding elements of eigenvectors. Specifically, canonical analysis is applied to an ill-conditioned case of singular within-sets input matrices encompassing tourism accommodation data.

正则化是一种广为人知的常用统计方法，涵盖单个点或极限近似值。在这项研究中，路径的典型相关分析 (CCA) 过程与偏最小二乘法 (PLS) 作为另一个边界覆盖变换到取决于参数的对称特征值（或奇异值）问题进行了讨论。比较参数化域中原始标准的两种正则化，即使用投影和单位矩阵。我们讨论了特征值和特征向量对应元素的解析路径的存在性和唯一性。具体来说，将规范分析应用于包含旅游住宿数据的奇异组内输入矩阵的病态情况。