Consistency and diversity neural network multi-view multi-label learning

[1]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[2]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[3]  Qingming Huang,et al.  Improving multi-label classification with missing labels by learning label-specific features , 2019, Inf. Sci..

[4]  Hamido Fujita,et al.  A study of graph-based system for multi-view clustering , 2019, Knowl. Based Syst..

[5]  Qingming Huang,et al.  Joint multi-view representation and image annotation via optimal predictive subspace learning , 2018, Inf. Sci..

[6]  Dawei Zhao,et al.  Multi-label learning with kernel extreme learning machine autoencoder , 2019, Knowl. Based Syst..

[7]  Bernhard Schölkopf,et al.  Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.

[8]  Weiwei Li,et al.  Tensor-based multi-view label enhancement for multi-label learning , 2020, IJCAI.

[9]  Lu Sun,et al.  Multi-label classification with meta-label-specific features , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[10]  Qinghua Hu,et al.  Multi-view label embedding , 2018, Pattern Recognit..

[11]  Rong Jin,et al.  Correlated Label Propagation with Application to Multi-label Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Zhen Wang,et al.  Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels , 2014, 2014 IEEE International Conference on Data Mining.

[13]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[14]  Min-Ling Zhang,et al.  Lift: Multi-Label Learning with Label-Specific Features. , 2015, IEEE transactions on pattern analysis and machine intelligence.

[15]  Zhiming Luo,et al.  Towards a unified multi-source-based optimization framework for multi-label learning , 2019, Appl. Soft Comput..

[16]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[17]  Jing-Yu Yang,et al.  Multi-label learning with label-specific feature reduction , 2016, Knowl. Based Syst..

[18]  Philip S. Yu,et al.  Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation , 2020, IEEE Transactions on Multimedia.

[19]  Qingming Huang,et al.  Multi-View Multi-Label Learning With View-Label-Specific Features , 2019, IEEE Access.

[20]  Shiping Wen,et al.  Multilabel Image Classification via Feature/Label Co-Projection , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Xuelong Li,et al.  Block-Row Sparse Multiview Multilabel Learning for Image Classification , 2016, IEEE Transactions on Cybernetics.

[22]  Lin Ma,et al.  Global and local multi-view multi-label learning with incomplete views and labels , 2020, Neural Computing and Applications.

[23]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[24]  Dit-Yan Yeung,et al.  Multilabel relationship learning , 2013, TKDD.

[25]  Clara Pizzuti,et al.  A Multi-objective Genetic Algorithm for Community Detection in Networks , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[26]  Robert P. W. Duin,et al.  Feedforward neural networks with random weights , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[27]  Yao Hu,et al.  Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation , 2020, AAAI.

[28]  Lijuan Sun,et al.  Weakly-supervised multi-label learning with noisy features and incomplete labels , 2020, Neurocomputing.

[29]  Ligang Liu,et al.  Projective Feature Learning for 3D Shapes with Multi‐View Depth Images , 2015, Comput. Graph. Forum.

[30]  Jason Weston,et al.  A kernel method for multi-labelled classification , 2001, NIPS.

[31]  Donald W. Marquaridt Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation , 1970 .

[32]  Bo Jiang,et al.  Robust Mapping Learning for Multi-view Multi-label Classification with Missing Labels , 2017, KSEM.

[33]  Eyke Hüllermeier,et al.  Multilabel classification via calibrated label ranking , 2008, Machine Learning.

[34]  Zili Zhang,et al.  Multi-view Weak-label Learning based on Matrix Completion , 2018, SDM.

[35]  Yang Gao,et al.  Joint multi-label classification and label correlations with missing labels and feature selection , 2019, Knowl. Based Syst..

[36]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[37]  Xuran Zhao,et al.  A subspace co-training framework for multi-view clustering , 2014, Pattern Recognit. Lett..

[38]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[39]  Jie Lu,et al.  Structural property-aware multilayer network embedding for latent factor analysis , 2018, Pattern Recognit..

[40]  Xiangliang Zhang,et al.  Individuality- and Commonality-Based Multiview Multilabel Learning , 2019, IEEE Transactions on Cybernetics.