Iterative Aggregation Method for Solving Principal Component Analysis Problems
This work addresses the problem of computational efficiency in PCA for researchers and practitioners handling large-scale data, but it appears incremental as it builds on existing multilevel aggregation methods.
The authors tackled the computational challenge of solving Principal Component Analysis (PCA) problems by proposing a novel two-level aggregation approach, which was tested on large text document datasets and showed efficiency in iterative eigenvalue solutions.
Motivated by the previously developed multilevel aggregation method for solving structural analysis problems a novel two-level aggregation approach for efficient iterative solution of Principal Component Analysis (PCA) problems is proposed. The course aggregation model of the original covariance matrix is used in the iterative solution of the eigenvalue problem by a power iterations method. The method is tested on several data sets consisting of large number of text documents.