Global and local multi-view multi-label learning with incomplete views and labels

Multi-view multi-label learning is widely used in multiple fields, and it aims to process data sets represented by multiple forms (views) and labeled by multiple classes. But most real-world data sets maybe loss some labels and views due to lack of manpower and equipment failure and this causes some difficulties in processing data sets. In this paper, we develop a global and local multi-view multi-label learning with incomplete views and labels (GLMVML-IVL) to process this. In GLMVML-IVL, the usage of label-specific features indicates that class label is determined by some specific features rather than all features; global and local label correlations are taken into consideration with clustering technology; the construction of the pseudo-class label matrix offsets the defect of missing (partial) labels; the adoption of low-rank assumption matrix restores incomplete views; a consensus multi-view representation is put to use to encode the complementary information from different views; the regularizer imposed on label matrix reflects the partial pairwise constraints. Different from traditional methods, this is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, pseudo-class label matrix, low-rank assumption matrix, global and local label correlations, complementary information, and regularizer imposed on label matrix. Experimental results validate that GLMVML-IVL improves the performance of traditional multi-view multi-label learning methods in statistical and achieves a better performance.

[1]  Lei Wu,et al.  Lift: Multi-Label Learning with Label-Specific Features , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Qinghua Hu,et al.  Generalized Latent Multi-View Subspace Clustering , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  N. Jing,et al.  Geometric transformations of multidimensional color images based on NASS , 2016, Inf. Sci..

[4]  Jia Zhang,et al.  Multi-label learning with label-specific features by resolving label correlations , 2018, Knowl. Based Syst..

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

[6]  Yong Luo,et al.  Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Jieping Ye,et al.  A Reconstruction Error Based Framework for Multi-Label and Multi-View Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.

[8]  Yong Luo,et al.  Multiview Matrix Completion for Multilabel Image Classification , 2015, IEEE Transactions on Image Processing.

[9]  Naonori Ueda,et al.  Parametric Mixture Models for Multi-Labeled Text , 2002, NIPS.

[10]  Miki Haseyama,et al.  Multi-feature Fusion Based on Supervised Multi-view Multi-label Canonical Correlation Projection , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Duoqian Miao,et al.  Global and local multi-view multi-label learning , 2020, Neurocomputing.

[12]  Guoyin Wang,et al.  An active three-way clustering method via low-rank matrices for multi-view data , 2020, Inf. Sci..

[13]  Qinghua Hu,et al.  Semi-Supervised Multi-view Multi-label Classification Based on Nonnegative Matrix Factorization , 2017, ICANN.

[14]  Yu Liu,et al.  Multi-view multi-label learning for image annotation , 2015, Multimedia Tools and Applications.

[15]  Shunxiang Wu,et al.  Multi-label learning based on label-specific features and local pairwise label correlation , 2018, Neurocomputing.

[16]  Weili Wang,et al.  Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and biochemical dynamic optimization problems , 2017, Appl. Soft Comput..

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

[18]  Arun K. Pujari,et al.  Multi-label classification using hierarchical embedding , 2018, Expert Syst. Appl..

[19]  Quan-Sen Sun,et al.  Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition , 2014, Pattern Recognit..

[20]  Chun Chen,et al.  Multi-view based multi-label propagation for image annotation , 2015, Neurocomputing.

[21]  Xiaomin Zhu,et al.  A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly , 2016 .

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

[23]  Gert R. G. Lanckriet,et al.  Semantic Annotation and Retrieval of Music and Sound Effects , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[24]  Chang-Dong Wang,et al.  Weighted Multi-view Clustering with Feature Selection , 2016, Pattern Recognit..

[25]  Zhi-Hua Zhou,et al.  Multi-Label Learning with Global and Local Label Correlation , 2017, IEEE Transactions on Knowledge and Data Engineering.

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

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

[28]  Feiping Nie,et al.  Multiple view semi-supervised dimensionality reduction , 2010, Pattern Recognit..

[29]  Shu Wu,et al.  Multi-view clustering via joint feature selection and partially constrained cluster label learning , 2019, Pattern Recognit..

[30]  Dong Yue,et al.  Multi-view low-rank dictionary learning for image classification , 2016, Pattern Recognit..

[31]  Xiaofeng Zhu,et al.  Multi-view multi-sparsity kernel reconstruction for multi-class image classification , 2015, Neurocomputing.

[32]  Yong Luo,et al.  Multi-View Matrix Completion for Multi-Label Image Classification , 2019, ArXiv.

[33]  Qinghua Hu,et al.  Latent Semantic Aware Multi-View Multi-Label Classification , 2018, AAAI.

[34]  Yang Wang,et al.  Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning , 2014, IEEE Transactions on Image Processing.

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