Modularity Component Analysis versus Principal Component Analysis
This provides a method for data clustering in scenarios where centering is not feasible, but it appears incremental as it adapts an existing approach.
The paper tackled the problem of clustering data without requiring centering by establishing a linear relationship between modularity matrix eigenvectors and singular vectors of uncentered data, defining modularity components as an alternative to principal component analysis.
In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis can be used to cluster data similar to how traditional principal component analysis is used except that modularity component analysis does not require data centering.