TY - JOUR
T1 - Locally consistent concept factorization for document clustering
AU - Cai, Deng
AU - He, Xiaofei
AU - Han, Jiawei
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 60905001 and 90920303, National Key Basic Research Foundation of China under Grant 2009CB320801, the US National Science Foundation under Grants IIS-08-42769, IIS-09-05215, and the Air Force Office of Scientific Research MURI award FA9550-08-1-0265. Any opinions, findings, and conclusions expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.
PY - 2011
Y1 - 2011
N2 - Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.
AB - Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.
KW - Nonnegative matrix factorization
KW - clustering.
KW - concept factorization
KW - graph Laplacian
KW - manifold regularization
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U2 - 10.1109/TKDE.2010.165
DO - 10.1109/TKDE.2010.165
M3 - Article
AN - SCOPUS:79955525117
VL - 23
SP - 902
EP - 913
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 6
M1 - 5567104
ER -