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Matrix Completion for Graph-Based Deep Semi-Supervised Learning

Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi
<span title="2019-07-17">2019</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
In this paper, we introduce a new iterative Graph-based Semi-Supervised Learning (GSSL) method to train a CNN-based classifier using a large amount of unlabeled data and a small amount of labeled data.  ...  In this graph, the missing label of unsupervised nodes is predicted by using a matrix completion method based on rank minimization criterion.  ...  Related Work Deep Semi-Supervised Learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33015058">doi:10.1609/aaai.v33i01.33015058</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qc4pxvkto5gu7n5pe5ofo53qnq">fatcat:qc4pxvkto5gu7n5pe5ofo53qnq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200305175544/https://aaai.org/ojs/index.php/AAAI/article/download/4438/4316" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5b/61/5b61cb3f428378c02af6c5428e3a1c77ccd7788a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v33i01.33015058"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Noise-robust classification with hypergraph neural network

Nguyen Trinh Vu Dang, Loc Tran, Linh Tran
<span title="2021-03-01">2021</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trvfti3jm5hnxhei7rl7owpcqq" style="color: black;">Indonesian Journal of Electrical Engineering and Computer Science</a> </i> &nbsp;
Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph  ...  This method is utilized to solve the noisy label learning problem.  ...  . c) Compare the accuracy performance measures of the classic graph based semi-supervised learning problem, the classic hypergraph based semi-supervised learning problem, the graph neural network method  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v21.i3.pp1465-1473">doi:10.11591/ijeecs.v21.i3.pp1465-1473</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iz7g63by3vgofnrj6gocz5b4zy">fatcat:iz7g63by3vgofnrj6gocz5b4zy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210410001548/http://ijeecs.iaescore.com/index.php/IJEECS/article/download/23736/14719" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/28/b9/28b9778945a5f042ea17baee5eab9517d91e23ae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v21.i3.pp1465-1473"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Deep graph learning for semi-supervised classification [article]

Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao, Yajun Chen
<span title="2020-05-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To simulate the interdependence, deep graph learning(DGL) is proposed to find the better graph representation for semi-supervised classification.  ...  GCN for semi-supervised classification.  ...  The authors would like to thank the anonymous reviewers for their insightful comments that help improve the quality of this paper. This work was supported by NSFC  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.14403v1">arXiv:2005.14403v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oioedbp6cjg4bfu7chmf4cspqy">fatcat:oioedbp6cjg4bfu7chmf4cspqy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200602020255/https://arxiv.org/pdf/2005.14403v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.14403v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Image Representation Learning Using Graph Regularized Auto-Encoders [article]

Yiyi Liao, Yue Wang, Yong Liu
<span title="2014-02-19">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning.  ...  Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally  ...  Table 6 : Results of the semi-supervised learning tasks for ORL. Experiments in Semi-supervised Learning For semi-supervised learning task, a small part of the samples are labeled.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1312.0786v2">arXiv:1312.0786v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4bopdzckm5eddiqcrltj4y3yde">fatcat:4bopdzckm5eddiqcrltj4y3yde</a> </span>
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Semi-AttentionAE: An Integrated Model for Graph Representation Learning

Lining Yuan, Yang Wang, Xianggui Cheng, Zhao Liu
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In Semi-AttentionAE, GAT samples the graph structure and node features, and generates input matrix H for AE through supervised learning based on node labels.  ...  THE MODEL In this section, we introduce the proposed semi-supervised deep model Semi-AttentionAE.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3085114">doi:10.1109/access.2021.3085114</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lppcn2gzrnhvnnuxvzi6ck63nu">fatcat:lppcn2gzrnhvnnuxvzi6ck63nu</a> </span>
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Image Recognition and Analysis of Intrauterine Residues Based on Deep Learning and Semi-Supervised Learning

Tao Tao, Kan Liu, Li Wang, Haiying Wu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The core step of the semi-supervised spectral clustering algorithm based on deep learning is the adjustment of the similarity matrix.  ...  learning combined with semi-supervised learning algorithm FIGURE 3 . 3 Image recognition flow chart of intrauterine residue based on semi-supervised deep learning FIGURE 4 . 4 Deep learning neural  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3020322">doi:10.1109/access.2020.3020322</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mgkpfn7oozbp5actzq42bx5mtm">fatcat:mgkpfn7oozbp5actzq42bx5mtm</a> </span>
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Domain Adaptation with Adversarial Training and Graph Embeddings

Firoj Alam, Shafiq Joty, Muhammad Imran
<span title="">2018</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5n6volmnonf5tn6xputi5f2t3e" style="color: black;">Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</a> </i> &nbsp;
We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single  ...  unified deep learning framework.  ...  Semi-supervised Learning In Table 2 , we present the results obtained from the supervised, self-training based semi-supervised, and our graph-based semi-supervised experiments for the both datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p18-1099">doi:10.18653/v1/p18-1099</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl/JotyAI18.html">dblp:conf/acl/JotyAI18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fy5l3zyaare4np6xxesl7476ye">fatcat:fy5l3zyaare4np6xxesl7476ye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507191313/https://www.aclweb.org/anthology/P18-1099.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1b/d5/1bd578c4c310ee29a6fe810dbebf7e38512b8432.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p18-1099"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Domain Adaptation with Adversarial Training and Graph Embeddings [article]

Firoj Alam and Shafiq Joty and Muhammad Imran
<span title="2018-05-14">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single  ...  unified deep learning framework.  ...  Semi-supervised Learning In Table 2 , we present the results obtained from the supervised, self-training based semi-supervised, and our graph-based semi-supervised experiments for the both datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.05151v1">arXiv:1805.05151v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7rj47d6zlvcedkdvv4j46ekz2a">fatcat:7rj47d6zlvcedkdvv4j46ekz2a</a> </span>
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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Zhuangwei Shi, Han Zhang, Chen Jin, Xiongwen Quan, Yanbin Yin
<span title="2021-03-21">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
via a deep learning approach.  ...  Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations  ...  Variational inference for graph semi-supervised learning Graph semi-supervised learning Semi-supervised learning is based on manifold assumption [52] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04073-z">doi:10.1186/s12859-021-04073-z</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33745450">pmid:33745450</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7983260/">pmcid:PMC7983260</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zizhlllvovd77dkmx55kxlnjca">fatcat:zizhlllvovd77dkmx55kxlnjca</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210324031027/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-021-04073-z.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/04/a5/04a54b5346103118aa2e6dcc11f17b0a49a54cab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04073-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7983260" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data [article]

Fuchuan Tong, Siqi Zheng, Min Zhang, Yafeng Chen, Hongbin Suo, Qingyang Hong, Lin Li
<span title="2022-04-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we present a GCN-based approach for semi-supervised learning.  ...  Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning.  ...  GCN-BASED SEMI-SUPERVISED LEARNING GCN-based clustering In order to utilize a large amount of unlabeled meeting data for speaker recognition network training, a natural idea is to cluster the speakers  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.11501v1">arXiv:2204.11501v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4hjc5rgvxnhrjovhwpc4etjdau">fatcat:4hjc5rgvxnhrjovhwpc4etjdau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220428010922/https://arxiv.org/pdf/2204.11501v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/94/c2/94c28e930dab00fafb6a122f34f2e245e2c1d201.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.11501v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A review of various semi-supervised learning models with a deep learning and memory approach

Jamshid Bagherzadeh, Hasan Asil
<span title="2018-12-06">2018</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b4iekbmgnbaw5gr4hjudn7j6ca" style="color: black;">Iran Journal of Computer Science</a> </i> &nbsp;
Therefore, semi-supervised learning is more practical and useful for solving most of the problems.  ...  The aim of this paper was to analyze the available models of semi-supervised learning with an approach to deep learning.  ...  Graph-based methods Graph-based semi-supervised learning methods are based on the string theory.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s42044-018-00027-6">doi:10.1007/s42044-018-00027-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nccifurxyzc33fa5xfprrlupxq">fatcat:nccifurxyzc33fa5xfprrlupxq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504014357/https://link.springer.com/content/pdf/10.1007%2Fs42044-018-00027-6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7d/1f/7d1f7763d875795011b481e3c49461474bd08ae5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s42044-018-00027-6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer

Yugang Ma, Qing Li, Nan Hu, Lili Li
<span title="2021-04-01">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/el4ui6zhlfcjjbeubbsd7m4x6i" style="color: black;">Frontiers in Neurorobotics</a> </i> &nbsp;
Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry.  ...  In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed.  ...  For this purpose, we proposed SeBioGraph, a new semi-supervised deep learning method for the biomedical graphs via knowledge transfer.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnbot.2021.665055">doi:10.3389/fnbot.2021.665055</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33867966">pmid:33867966</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8047129/">pmcid:PMC8047129</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzr5w63ssrbm7jckoassikebtq">fatcat:wzr5w63ssrbm7jckoassikebtq</a> </span>
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Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Shengliang Pu, Yuanfeng Wu, Xu Sun, Xiaotong Sun
<span title="2021-02-02">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
The nascent graph representation learning has shown superiority for resolving graph data.  ...  Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships.  ...  Acknowledgments: The authors thank the editors and the anonymous reviewers for their insightful comments and helpful suggestions, which undoubtedly improve the quality of this manuscript.  ... 
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Label Efficient Semi-Supervised Learning via Graph Filtering [article]

Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan
<span title="2019-06-28">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples  ...  In this paper, we address label efficient semi-supervised learning from a graph filtering perspective.  ...  For graph-based semi-supervised learning, the key challenge is to exploit graph structures as well as other information especially data features to improve learning performance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.09993v3">arXiv:1901.09993v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oqwhmisb4bbstgak5y4gqgruyu">fatcat:oqwhmisb4bbstgak5y4gqgruyu</a> </span>
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GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning [article]

Ozsel Kilinc, Ismail Uysal
<span title="2018-02-08">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training.  ...  Our results show comparable performance with state-of-the-art generative approaches for semi-supervised learning on an easier-to-train, low-cost framework.  ...  In this paper, we propose a novel framework for semi-supervised learning which can be considered a variant of graph-based approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1705.07219v2">arXiv:1705.07219v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wo25ralrafe5jkhxqnkhecyt74">fatcat:wo25ralrafe5jkhxqnkhecyt74</a> </span>
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