MultiSpectralNet: Spectral Clustering Using Deep Neural Network for Multi-View Data

Multi-view data provide more comprehensive information than single views by providing different feature sets of the same object. Learning its data structure through spectral clustering has always been the mainstream of research. However, due to the limitation of its core graph theory, traditional spectral graph-based multi-view clustering algorithms are inapplicable for analyzing large-scale data sets. In this paper, we propose MultiSpectralNet (MvSN), a deep learning approach to spectral multi-view clustering, provides mapping multi-view data points to their fusion eigenvectors and can obtain a more accurate data structure by correcting the misleading information in the single views to a certain extent by feedback in the network training process. In addition, our model can cluster large multi-view data sets and provide cluster prediction for out-of-sample extension. We test ACC and normalized mutual information (NMI) of our method in clustering several artificial and real-world data sets, and the experimental results show that our method outperforms conventional compared state-of-the-art works.

[1]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[2]  Steffen Bickel,et al.  Multi-view clustering , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[3]  Qinghua Hu,et al.  Subspace clustering guided unsupervised feature selection , 2017, Pattern Recognit..

[4]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[6]  Maurizio Filippone,et al.  Mini-batch spectral clustering , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[7]  Feiping Nie,et al.  Discriminatively Embedded K-Means for Multi-view Clustering , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Mianxiong Dong,et al.  Eyes in the Dark: Distributed Scene Understanding for Disaster Management , 2017, IEEE Transactions on Parallel and Distributed Systems.

[9]  Victor C. M. Leung,et al.  Incomplete multi-view clustering via deep semantic mapping , 2018, Neurocomputing.

[10]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[11]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[12]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[13]  Xuelong Li,et al.  Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours , 2017, AAAI.

[14]  Xiaochun Cao,et al.  Diversity-induced Multi-view Subspace Clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[16]  Yung-Yu Chuang,et al.  Multiple Kernel Fuzzy Clustering , 2012, IEEE Transactions on Fuzzy Systems.

[17]  Athanasios V. Vasilakos,et al.  Multimedia Processing Pricing Strategy in GPU-Accelerated Cloud Computing , 2020, IEEE Transactions on Cloud Computing.

[18]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[19]  Shou-De Lin,et al.  Social Flocks: Simulating Crowds to Discover the Connection Between Spatial-Temporal Movements of People and Social Structure , 2018, IEEE Transactions on Computational Social Systems.

[20]  Lin Wu,et al.  Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering , 2016, IJCAI.

[21]  Zhuo Chen,et al.  Deep clustering: Discriminative embeddings for segmentation and separation , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Jianhua Li,et al.  Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks , 2018, IEEE Transactions on Network and Service Management.

[23]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[24]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

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

[26]  Cheng Deng,et al.  Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  Pengjiang Qian,et al.  Collaborative Fuzzy Clustering From Multiple Weighted Views , 2015, IEEE Transactions on Cybernetics.

[28]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[29]  Qingshan Liu,et al.  Image retrieval via probabilistic hypergraph ranking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Ronen Basri,et al.  SpectralNet: Spectral Clustering using Deep Neural Networks , 2018, ICLR.

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

[32]  Hal Daumé,et al.  A Co-training Approach for Multi-view Spectral Clustering , 2011, ICML.

[33]  Yun Fu,et al.  Multi-View Clustering via Deep Matrix Factorization , 2017, AAAI.

[34]  Feiping Nie,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Multi-View K-Means Clustering on Big Data , 2022 .

[35]  MengChu Zhou,et al.  Clustering-Algorithm-Based Rare-Event Evolution Analysis via Social Media Data , 2019, IEEE Transactions on Computational Social Systems.

[36]  Yanmei Zhang,et al.  A Novel Hybrid Clustering Algorithm for Topic Detection on Chinese Microblogging , 2019, IEEE Transactions on Computational Social Systems.

[37]  Alexander Cloninger,et al.  Diffusion Nets , 2015, Applied and Computational Harmonic Analysis.

[38]  Christoph H. Lampert,et al.  Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Jiwen Lu,et al.  Nonlinear subspace clustering for image clustering , 2017, Pattern Recognit. Lett..

[40]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[41]  R. Coifman,et al.  Geometric harmonics: A novel tool for multiscale out-of-sample extension of empirical functions , 2006 .

[42]  Raquel Urtasun,et al.  Deep Spectral Clustering Learning , 2017, ICML.

[43]  Xuelong Li,et al.  Multiview Clustering via Adaptively Weighted Procrustes , 2018, KDD.