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Learning from partially labeled data

Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
2020 The European Symposium on Artificial Neural Networks  
In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among others.  ...  There are several possibilities for designing such models ranging from shallow to deep models.  ...  The semi-supervised learning, domain adaption, multi-view learning are among existing proposed models. Semi-supervised models use both labeled and unlabeled data points in the learning process.  ... 
dblp:conf/esann/MehrkanoonHS20 fatcat:hdjcnwwu4fgzbjwv5uotkcyvua

A survey of multi-view machine learning

Shiliang Sun
2013 Neural computing & applications (Print)  
This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research.  ...  Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success.  ...  Multi-view supervised learning Unlike semi-supervised learning, supervised learning only uses labeled data for function learning.  ... 
doi:10.1007/s00521-013-1362-6 fatcat:kzt7hibfo5axheedlaofw3pb7m

Multi-level Consistency Learning for Semi-supervised Domain Adaptation [article]

Zizheng Yan, Yushuang Wu, Guanbin Li, Yipeng Qin, Xiaoguang Han, Shuguang Cui
2022 arXiv   pre-print
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.  ...  In this paper, we propose a Multi-level Consistency Learning (MCL) framework for SSDA.  ...  in label-scarce learning scenarios, e.g. domain adaptation, semi-supervised learning.  ... 
arXiv:2205.04066v3 fatcat:4l65qasp4jdfrjxmpmhm3fo4bq

ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition [article]

Zekai Chen, Xiao Zhang, Xiuzhen Cheng
2022 arXiv   pre-print
To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning.  ...  Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multi-view learning problem.  ...  Semi Supervised Multi-Task Multi-View Learning for Fragmented Time Series with Gathering Consistency Adaption Input: Labeled multi-task multi-view time series data Xs, unlabeled data Xu, number of tasks  ... 
arXiv:2105.08643v2 fatcat:sx3hzhlk55hprcoufh6qnte4zi

TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification [article]

Wei Zhang, Zhaohong Deng, Qiongdan Lou, Te Zhang, Kup-Sze Choi, Shitong Wang
2021 arXiv   pre-print
In this paper, a transductive semi-supervised incomplete multi-view TSK fuzzy system modeling method (SSIMV_TSK) is proposed to address these challenges.  ...  Traditional multi-view learning methods rely on a large number of labeled and completed multi-view data.  ...  The framework of the proposed semi-supervised incomplete multi-view learning for TSK fuzzy systems.  ... 
arXiv:2110.05610v3 fatcat:6idaq5kuanbfrpfxc3uzuysx5a

Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks [article]

Canyu Yang, Dennis Eschweiler, Johannes Stegmaier
2021 arXiv   pre-print
Compared with classical state-of-the-art methods, the proposed semi- and self-supervised models achieve competitive and superior deconvolution and fusion quality in the two-view and quad-view cases, respectively  ...  Lately, Convolutional Neural Networks (CNNs) have been deployed to approach 3D single-view deconvolution microscopy, but the multi-view case waits to be studied.  ...  our learning-based pipelines for multi-view deconvolution and fusion that are presented in the next section.  ... 
arXiv:2108.02743v1 fatcat:hmstzzjqvvhifnle5sgq7p524m

Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion [article]

Yang Wang
2020 arXiv   pre-print
With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects.  ...  Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces.  ...  [21] proposed structured generative adversarial networks (SGANs) for semi-supervised image classification.  ... 
arXiv:2006.08159v1 fatcat:g4467zmutndglmy35n3eyfwxku

Deep Multi-view Semi-supervised Clustering with Sample Pairwise Constraints [article]

Rui Chen, Yongqiang Tang, Wensheng Zhang, Wenlong Feng
2022 arXiv   pre-print
multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss.  ...  To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including  ...  Acknowledgment The authors are thankful for the financial support by the National Key Research and Development Program of China (2020AAA0109500), the Key-Area Research and Development Program of Guangdong  ... 
arXiv:2206.04949v1 fatcat:s3vmjwaxznh23hhhzh65oskz3e

Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

Alim Samat, Claudio Persello, Paolo Gamba, Sicong Liu, Jilili Abuduwaili, Erzhu Li
2017 Remote Sensing  
In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous  ...  Accordingly, this work introduces an EL technique based on supervised multi-view CCA, which is called supervised multi-view canonical correlation analysis ensemble (SMVCCAE), and we prove its effectiveness  ...  For new research directions, we are considering more complex problems, such as single SD vs. multiple TDs, as well as multiple SDs vs. multiple TDs supervised and semi-supervised adaptation techniques.  ... 
doi:10.3390/rs9040337 fatcat:cq27i7bnzfdk7ot2emgnfgmwpi

Multi-view learning overview: Recent progress and new challenges

Jing Zhao, Xijiong Xie, Xin Xu, Shiliang Sun
2017 Information Fusion  
Multi-view learning is an emerging direction in machine learning which considers learning with multiple views to improve the generalization performance.  ...  The main feature of this survey is that we provide comprehensive introduction for the recent developments of multi-view learning methods on the basis of coherence with early methods.  ...  They made generalization error analysis for both supervised and semi-supervised multi-view learning methods.  ... 
doi:10.1016/j.inffus.2017.02.007 fatcat:6we7lm2buncftg7xzym63ncxmy

Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours

Feiping Nie, Guohao Cai, Xuelong Li
2017 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously.  ...  Generally, these learning algorithms construct informative graph for each view or fuse different views to one graph, on which the following procedure are based.  ...  In this paper, we propose a novel multi-view learning model, named Multi-view Learning with Adaptive Neighbours (MLAN).  ... 
doi:10.1609/aaai.v31i1.10909 fatcat:ega6tsy6ara3pngzltjqgzftge

Latent Multi-view Semi-Supervised Classification [article]

Xiaofan Bo and Zhao Kang and Zhitong Zhao and Yuanzhang Su and Wenyu Chen
2019 arXiv   pre-print
Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and  ...  To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method.  ...  Acknowledgments This paper was in part supported by Grants from the Natural Science Foundation of China (Nos. 61806045 and 61572111), two Fundamental Research Fund for the Central Universities of China  ... 
arXiv:1909.03712v1 fatcat:bvhxdl5345g6xkaciieadf5oqi

Multi-Augmentation for Efficient Visual Representation Learning for Self-supervised Pre-training [article]

Van-Nhiem Tran, Chi-En Huang, Shen-Hsuan Liu, Kai-Lin Yang, Timothy Ko, Yung-Hui Li
2022 arXiv   pre-print
In this work, we proposed Multi-Augmentations for Self-Supervised Representation Learning (MA-SSRL), which fully searched for various augmentation policies to build the entire pipeline to improve the robustness  ...  MA-SSRL successfully learns the invariant feature representation and presents an efficient, effective, and adaptable data augmentation pipeline for self-supervised pre-training on different distribution  ...  will be generated for each image.  ... 
arXiv:2205.11772v1 fatcat:rnkck5nuwba3valjbw6dfqgux4

Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification

Zhe Xue, Junping Du, Dawei Du, Wenqi Ren, Siwei Lyu
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification.  ...  Specifically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unified framework to jointly learn the deep correlated predictive  ...  Up to now, several approaches have been developed for the incomplete multi-view unsupervised or semi-supervised learning.  ... 
doi:10.24963/ijcai.2019/559 dblp:conf/ijcai/XueDDRL19 fatcat:jzqvhry3rvahngqygepjjmkd4i

Co-GCN for Multi-View Semi-Supervised Learning

Shu Li, Wen-Tao Li, Wei Wang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we bring Graph Convolutional Network (GCN) into multi-view learning and propose a novel multi-view semi-supervised learning method Co-GCN by adaptively exploiting the graph information from  ...  Experimental results on real-world data sets verify that Co-GCN can achieve better performance compared with state-of-the-art multi-view semi-supervised methods.  ...  Acknowledgements The authors would like to thank Ching-Yun Ko for helpful discussions.  ... 
doi:10.1609/aaai.v34i04.5901 fatcat:pzwpbstn75ee7o2b2utv2y5bca
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