Multi-view Information-theoretic Co-clustering for Co-occurrence Data

@inproceedings{Xu2019MultiviewIC,
  title={Multi-view Information-theoretic Co-clustering for Co-occurrence Data},
  author={Peng Xu and Zhaohong Deng and Kup-Sze Thomas Choi and Longbing Cao and Shitong Wang},
  booktitle={AAAI},
  year={2019}
}
Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e… 

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References

SHOWING 1-10 OF 31 REFERENCES

Collaborative Fuzzy Clustering From Multiple Weighted Views

TLDR
Extensive experimental results indicate that the proposed WV-Co-FCM algorithm outperforms or is at least comparable to the existing state-of-the-art multitask and multiview clustering algorithms and the importance of different views of the datasets can be effectively identified.

Co-regularized Multi-view Spectral Clustering

TLDR
A spectral clustering framework is proposed that achieves this goal by co-regularizing the clustering hypotheses, and two co- regularization schemes are proposed to accomplish this.

Multi-view Self-Paced Learning for Clustering

TLDR
A new multi-view self-paced learning (MSPL) algorithm for clustering is presented, that learns the multi-View model by not only progressing from 'easy' to 'complex' examples, but also from ' easy' to'complex' views.

Multiple Kernel Learning Based Multi-view Spectral Clustering

TLDR
A multiple kernel spectral clustering algorithm is proposed that can determine the kernel weights and cluster the multi-view data simultaneously and is compared with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

A Co-training Approach for Multi-view Spectral Clustering

TLDR
A spectral clustering algorithm for the multi-view setting where the authors have access to multiple views of the data, each of which can be independently used for clustering, which has a flavor of co-training.

Kernel-Based Weighted Multi-view Clustering

TLDR
This work exploits multiple representations for the same set of instances within a clustering framework in terms of given kernel matrices and a weighted combination of the kernels is learned in parallel to the partitioning.

Clustering Large and Sparse Co-occurrence Data

TLDR
This paper shows that sparse high-dimensional data presents special challenges which can result in the algorithm getting stuck at poor local minima, and proposes two solutions: a prior to overcome infinite relative entropy values as in the supervised Naive Bayes algorithm, and a local search to escapeLocal minima.

Co-regularized PLSA for Multi-view Clustering

TLDR
An extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA), which integrates individual PLSAs in different views by pairwise co-regularization.

Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

  • Quanquan GuJie Zhou
  • Computer Science
    2009 Ninth IEEE International Conference on Data Mining
  • 2009
TLDR
A novel clustering paradigm, namely multi-task clustering, which performs multiple related clustering tasks together and utilizes the relation of these tasks to enhance the clustering performance, and which is comparable to or even better than several existing transductive transfer classification approaches.