Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering

	Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering
	Bing-Kun Bao; Weiqing Min ; Teng Li ; Changsheng Xu
发表期刊	IEEE Transactions on Cybernetics
	2015-01
卷号	45 期号:1 页码:15-28
摘要	Co-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.
关键词	Co-clustering Information Theory Local And Global Learning
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11021
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.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
推荐引用方式 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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