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Learning from partially labeled data
2020
The European Symposium on Artificial Neural Networks
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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
2013
Neural computing & applications (Print)
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2017
Remote Sensing
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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
2017
Information Fusion
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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
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2019
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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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
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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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