Sparse subspace representation for spectral document clustering
Saha, Budhaditya, Phung, Dinh, Pham, Duc Son and Venkatesh, Svetha 2012, Sparse subspace representation for spectral document clustering, in ICDM 2012 : Proceedings of the 12th IEEE International Conference on Data Mining, IEEE, Piscataway, N.J., pp. 1092-1097, doi: 10.1109/ICDM.2012.46.
Attached Files
Name
Description
MIMEType
Size
Downloads
Title
Sparse subspace representation for spectral document clustering
We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.