Adaptive Graph Completion Based Incomplete Multi-View Clustering

In real-world applications, it is often that the collected multi-view data are incomplete, i.e., some views of samples are absent. Existing clustering methods for incomplete multi-view data all focus on obtaining a common representation or graph from the available views but neglect the hidden information of missing views and information imbalance of different views. To solve these problems, a novel method, called adaptive graph completion based incomplete multi-view clustering (AGC_IMC), is proposed in this paper. Specifically, AGC_IMC develops a joint framework for graph completion and consensus representation learning, which mainly contains three components, i.e., within-view preservation, between-view inferring, and consensus representation learning. To reduce the negative influence of information imbalance, AGC_IMC introduces some adaptive weights to adaptively balance the importance of different views during the consensus representation learning. Importantly, AGC_IMC has the potential to recover the similarity graphs of all views with the optimal cluster structure, which encourages it to obtain a more discriminative consensus representation. Experimental results on five well-known datasets show that AGC_IMC significantly outperforms the state-of-the-art methods in terms of clustering accuracy (14%-17%), normalized mutual information (10%-16%) and purity (10%-15%)

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Derek Greene,et al.  Practical solutions to the problem of diagonal dominance in kernel document clustering , 2006, ICML.

[3]  Huazhu Fu,et al.  CPM-Nets: Cross Partial Multi-View Networks , 2019, NeurIPS.

[4]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[5]  Philip S. Yu,et al.  Online multi-view clustering with incomplete views , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[6]  Feiping Nie,et al.  A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering , 2015, IJCAI.

[7]  Lloyd N. Trefethen,et al.  Large-Scale Computation of Pseudospectra Using ARPACK and Eigs , 2001, SIAM J. Sci. Comput..

[8]  Feiping Nie,et al.  Large-Scale Multi-View Spectral Clustering via Bipartite Graph , 2015, AAAI.

[9]  Chunwei Tian,et al.  Image denoising using deep CNN with batch renormalization , 2020, Neural Networks.

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Hao Wang,et al.  Spectral Perturbation Meets Incomplete Multi-view Data , 2019, IJCAI.

[12]  Xiaochun Cao,et al.  Low-Rank Tensor Constrained Multiview Subspace Clustering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Zhenyu He,et al.  Dual Pursuit for Subspace Learning , 2019, IEEE Transactions on Multimedia.

[14]  Dinggang Shen,et al.  Late Fusion Incomplete Multi-View Clustering , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Christopher Conly,et al.  Deep Learning Based HEVC In-Loop Filtering for Decoder Quality Enhancement , 2018, 2018 Picture Coding Symposium (PCS).

[16]  M. Cugmas,et al.  On comparing partitions , 2015 .

[17]  Yun Fu,et al.  Incomplete Multi-Modal Visual Data Grouping , 2016, IJCAI.

[18]  Yun Fu,et al.  Partial Multi-view Clustering via Consistent GAN , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[19]  Piyush Rai,et al.  Multiview Clustering with Incomplete Views , 2010 .

[20]  Xiaozhao Fang,et al.  Protein fold recognition based on multi-view modeling , 2019, Bioinform..

[21]  Jane You,et al.  Shared Linear Encoder-Based Multikernel Gaussian Process Latent Variable Model for Visual Classification , 2019, IEEE Transactions on Cybernetics.

[22]  Xin Wang,et al.  Robust Auto-Weighted Multi-View Clustering , 2018, IJCAI.

[23]  Hong Liu,et al.  Incomplete Multiview Spectral Clustering With Adaptive Graph Learning , 2020, IEEE Transactions on Cybernetics.

[24]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[25]  Feiping Nie,et al.  Multiview Consensus Graph Clustering , 2019, IEEE Transactions on Image Processing.

[26]  Henggui Zhang,et al.  Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images , 2018, IEEE Transactions on Biomedical Engineering.

[27]  K. R. Rao,et al.  Adaptive CU Mode Selection in HEVC Intra Prediction: A Deep Learning Approach , 2019, Circuits Syst. Signal Process..

[28]  Philip S. Yu,et al.  Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization , 2015, ECML/PKDD.

[29]  Shiliang Sun,et al.  A Survey on Multiview Clustering , 2017, IEEE Transactions on Artificial Intelligence.

[30]  Chong-Wah Ngo,et al.  Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint , 2008, IEEE Transactions on Multimedia.

[31]  Yicong Zhou,et al.  Jointly Learning Kernel Representation Tensor and Affinity Matrix for Multi-View Clustering , 2020, IEEE Transactions on Multimedia.

[32]  Cai Xu,et al.  Adversarial Incomplete Multi-view Clustering , 2019, IJCAI.

[33]  Chang Tang,et al.  Efficient and Effective Regularized Incomplete Multi-View Clustering , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Shao-Yuan Li,et al.  Partial Multi-View Clustering , 2014, AAAI.

[35]  Zheng Zhang,et al.  Adaptive Locality Preserving Regression , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[36]  Hao Wang,et al.  Multi-view clustering: A survey , 2018, Big Data Min. Anal..

[37]  Jian Yang,et al.  Adaptive weighted nonnegative low-rank representation , 2018, Pattern Recognit..

[38]  Xinwang Liu,et al.  Learning a Joint Affinity Graph for Multiview Subspace Clustering , 2019, IEEE Transactions on Multimedia.

[39]  Feiping Nie,et al.  The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.

[40]  Juho Rousu,et al.  Multi-view kernel completion , 2016, Machine Learning.

[41]  Jun Guo,et al.  Partial Multi-View Outlier Detection Based on Collective Learning , 2018, AAAI.

[42]  Xuelong Li,et al.  Multi-view Subspace Clustering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[43]  Zi Huang,et al.  Scalable Supervised Asymmetric Hashing With Semantic and Latent Factor Embedding , 2019, IEEE Transactions on Image Processing.

[44]  Andrew B. Kahng,et al.  New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[45]  Santanu Chaudhury,et al.  Partial Multi-View Clustering using Graph Regularized NMF , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[46]  Chunwei Tian,et al.  Low-rank representation with adaptive graph regularization , 2018, Neural Networks.

[47]  Yong Dou,et al.  Absent Multiple Kernel Learning Algorithms , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Xinwang Liu,et al.  Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection , 2019, AAAI.

[49]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[50]  David Zhang,et al.  Generative multi-view and multi-feature learning for classification , 2018, Inf. Fusion.

[51]  Jie Wu,et al.  Incomplete Multi-view Clustering via Structured Graph Learning , 2018, PRICAI.

[52]  Songcan Chen,et al.  One-Pass Incomplete Multi-view Clustering , 2019, AAAI.

[53]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[54]  Hong Liu,et al.  Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering , 2019, AAAI.

[55]  Ananda S. Chowdhury,et al.  Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest Clustering , 2015, IEEE Transactions on Multimedia.

[56]  Zheng Zhang,et al.  Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion , 2020, IEEE Transactions on Cybernetics.

[57]  Bin Liu,et al.  Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[58]  Lei Wang,et al.  Multiple Kernel k-Means with Incomplete Kernels , 2017, AAAI.

[59]  Wangmeng Zuo,et al.  Attention-guided CNN for image denoising , 2020, Neural Networks.

[60]  Songcan Chen,et al.  Doubly Aligned Incomplete Multi-view Clustering , 2018, IJCAI.