Topic Identification in Soft Clustering using PCA and ICA
Leonid E. Zhukov, David F. Gleich, Harvey Mudd · 2004
Many applications can benefit from soft clustering, where each datum is assigned to multiple clusters with membership weights that sum to one. In this paper we present a comparison of principal component analysis (PCA) and independent component analysis (ICA) when used for soft clustering. We provide a short mathematical background for these methods and demonstrate their application to a sponsored links search listings dataset. We present examples of the soft clusters generated by both methods and compare the results.