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Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering
Bing-Kun Bao; Weiqing Min; Teng Li; Changsheng Xu
Source PublicationIEEE Transactions on Cybernetics
2015-01
Volume45Issue:1Pages:15-28
AbstractCo-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint
on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called coclustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by
a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.
KeywordCo-clustering Information Theory Local And Global Learning
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11021
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Academy of Sciences, National Laboratory of Pattern Recognition, Beijing, China
2.Chinese Academy of Sciences, National Laboratory of Pattern Recognition, Beijing, China
3.College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
4.Chinese Academy of Sciences, National Laboratory of Pattern Recognition, Beijing, China
Recommended Citation
GB/T 7714
Bing-Kun Bao,Weiqing Min,Teng Li,et al. Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering[J]. IEEE Transactions on Cybernetics,2015,45(1):15-28.
APA Bing-Kun Bao,Weiqing Min,Teng Li,&Changsheng Xu.(2015).Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering.IEEE Transactions on Cybernetics,45(1),15-28.
MLA Bing-Kun Bao,et al."Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering".IEEE Transactions on Cybernetics 45.1(2015):15-28.
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