Singular Value Decomposition for dimensionality reduction in unsupervised text learning problems
Taufik Fuadi Abidin, Bustami Yusuf, Munzir Umran · 2010
Partitioning vast amounts of text documents is a challenging problem due to a high dimensional representation of the documents. In this study, we investigate the quality of text document clustering when Singular Value Decomposition (SVD) is used to reduce the dimension of the documents. The results show that the quality of the clusters is very comparable to that of when the dimensions are not reduced. In addition, the computational cost to cluster documents can be reduced significantly when the clustering is done on a small dimension.