Splitting Technique Initialization in Local PCA
Kuldip K. Paliwal
Godfrey C. Onwubolu
The local Principal Component Analysis (PCA) reduces linearly redundant components that may present in higher dimensional space. It deploys an initial guess technique which can be utilized when the distribution of a given multivariate data is known to the user. The problem in initialization arises when the distribution is not known. This study explores a technique that can be easily integrated in the local PCA design and is efficient even when the given statistical distribution is unknown. The initialization using this proposed splitting technique not only splits and reproduces the mean vector but also the orientation of components in the subspace domain. This would ensure that all clusters are used in the design. The proposed integration with the reconstruction distance local PCA design enables easier data processing and more accurate representation of multivariate data. A comparative approach is undertaken to demonstrate the greater effectiveness of the proposed approach in terms of percentage error.