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Multi-modal Sentiment Classification with Independent and Interactive Knowledge via Semi-supervised Learning
2020
IEEE Access
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In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning. ...
Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification. ...
. • Tri-training: A widely used semi-supervised classification approach for multi-view learning [24] , which addresses the problem of lacking of labeled samples effectively. ...
doi:10.1109/access.2020.2969205
fatcat:bkcydvpc7bgs3l6n4usfz7lvcy
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2204.02399v1 fatcat:selju7yz5zdn3pvxqe3xbqhcyq
In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis. ...
Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. ...
We consider the problem of hypergraph regularisation for semi-supervised classification [31, 15, 11] , where we aim to find a function u * to infer labels for the unlabelled set and enforce smoothness ...
arXiv:2204.02399v2
fatcat:roc3is2pz5fbnbmo6q7copmzia
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. ...
Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. ...
regression for semi-supervised classification [9] , A label propagation [10] , nonlinear embedding in deep multi-layer architectures [11] and Semi-Supervised Kernel spectral clustering [2] . ...
dblp:conf/esann/MehrkanoonHS20
fatcat:hdjcnwwu4fgzbjwv5uotkcyvua
A New SSOPMV Learning for Matrix Data Sets
2018
IOP Conference Series: Materials Science and Engineering
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Related experiments validate that MSSOPMV can process multi-view, semi-supervised, large-scale, and matrix data sets well. ...
In order to process these data sets, scholars have developed semi-supervise done-pass multi-view learning (SSOPMV). While SSOPMV cannot process matrix data sets. ...
learning machines to process the related semi-supervised data sets. ...
doi:10.1088/1757-899x/466/1/012111
fatcat:7sijfw2f5fg5poc2xpvrr7xciy
Dual Relation Semi-Supervised Multi-Label 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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To this end, we proposed a Dual Relation Semi-supervised Multi-label Learning (DRML) approach which jointly explores the feature distribution and the label relation simultaneously. ...
Multi-label learning (MLL) solves the problem that one single sample corresponds to multiple labels. ...
Conclusion In this paper, we proposed a Dual Relation Multi-label Learning (DRML) approach for Semi-supervised manner. ...
doi:10.1609/aaai.v34i04.6089
fatcat:kn3rlarcyfekxnfttiggqrfr3i
Parameterized Semi-supervised Classification Based on Support Vector for Multi-relational Data
[chapter]
2006
Lecture Notes in Computer Science
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A Parameterized Semi-supervised Classification algorithm based on Support Vector (PSCSV) for multi-relational data is presented in this paper. ...
Data is labeled according to its affinity to class centers. A novel Kernel function encoded in PSCSV is defined for multi-relational version and parameterized by supervisory information. ...
With this weak label information or sideinformation, classification issue is equivalent to semi-supervised clustering where the decision model is learned from all data. ...
doi:10.1007/11881070_10
fatcat:ii2x2bybxjavrjukssqljun7fi
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation
2015
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
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In this work, we propose a dual-view co-training algorithm based on dual-view BOW representation for semisupervised sentiment classification. ...
The experimental results demonstrate the advantages of our approach, in meeting the two co-training requirements, addressing the negation problem, and enhancing the semi-supervised sentiment classification ...
In comparison, semi-supervised sentiment classification has much less related studies. In this section, we focus on reviewing the work of semi-supervised sentiment classification. ...
doi:10.3115/v1/p15-1102
dblp:conf/acl/XiaWDL15
fatcat:fbshd5hpfvecfgn25tpxt4msim
KeCo: Kernel-Based Online Co-agreement Algorithm
[chapter]
2015
Lecture Notes in Computer Science
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We propose a kernel-based online semi-supervised algorithm that is applicable for large scale learning tasks. ...
In particular, we use a multi-view learning framework and a co-agreement strategy to take into account unlabelled data and to improve classification performance of the algorithm. ...
Conclusion This work presents a kernel-based online co-agreement algorithm applicable to large scale semi-supervised classification tasks. ...
doi:10.1007/978-3-319-24282-8_26
fatcat:4tchsmtyere2tkdwxvdr4mhzyq
Tractable Semi-supervised Learning of Complex Structured Prediction Models
[chapter]
2013
Lecture Notes in Computer Science
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In this paper, we propose an approximate semi-supervised learning method that uses piecewise training for estimating the model weights and a dual decomposition approach for solving the inference problem ...
As an example, we apply this approach to the problem of multi-label classification (a fully connected pairwise Markov random field). ...
In addition, some works have been conducted to study semi-supervised learning approach for multi-label classification problems [4, 8, 22, 38] . ...
doi:10.1007/978-3-642-40994-3_12
fatcat:i5oud3x74rav5hnxnj7kr4wirm
Semi-supervised remote sensing image classification via maximum entropy
2010
2010 IEEE International Workshop on Machine Learning for Signal Processing
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In this paper, we evaluate semi-supervised logistic regression (SLR), a recent information theoretic semi-supervised algorithm, for remote sensing image classification problems. ...
While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and ...
This gives the basis for the derivations of the semi-supervised logistic regression algorithm presented in Section 3, as a particular instance of a family of semi-supervised learning methods motivated ...
doi:10.1109/mlsp.2010.5589199
fatcat:sfzutkmgvzcvzm3hpj325nzhxq
Semi-supervised Medical Image Segmentation through Dual-task Consistency
[article]
2021
arXiv
pre-print
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To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time. ...
Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. ...
Clinical Center for the publicly available datasets. ...
arXiv:2009.04448v2
fatcat:x7bsdzugcvfdjbyrb4uqxyobr4
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
[article]
2021
arXiv
pre-print
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To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images ...
Extensive experiments showed that our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins, demonstrating ...
Dual-consistency Learning for Semi-supervised Segmentation Image-level Consistency Learning via transformation equivalence of deep segmentation models f seg indicates that while a transformation T (·) ...
arXiv:2104.03225v2
fatcat:shaahzvnafgo5ivx5wptcvcste
Dual word and document seed selection for semi-supervised sentiment classification
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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Semi-supervised sentiment classification aims to train a classifier with a small number of labeled data (called seed data) and a large amount of unlabeled data. a big advantage of this approach is its ...
In this paper, we propose an approach to further minimize the annotation effort of semi-supervised sentiment classification by actively selecting the seed data. ...
We also thank the two anonymous reviewers for their helpful comments. ...
doi:10.1145/2396761.2398624
dblp:conf/cikm/JuLSZHL12
fatcat:wy7e4igy7vhh7atcdxl43e5amy
Adversarial Autoencoder and Multi-Task Semi-Supervised Learning for Multi-stage Process
[chapter]
2020
Lecture Notes in Computer Science
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We redesign the multi-stage problem to address both cases by combining adversarial autoencoders (AAE) and multi-task semi-supervised learning (MTSSL) to train an end-to-end neural network for all stages ...
This is represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers for this case is challenging. ...
Multi-Task and Semi-Supervised Learning -The goal of multi-task learning (MTL) is to learn multiple related tasks simultaneously so that knowledge obtained from each task can be re-used by the others. ...
doi:10.1007/978-3-030-47436-2_1
fatcat:plxyif2uujghxamettv6ygfocy
Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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Weakly-supervised methods, on the other hand, only require image-level labels for training, but the performance is far below their fully-supervised counterparts. ...
In this paper, we propose a semi-supervised large scale fine-grained detection method, which only needs bounding box annotations of a smaller number of coarsegrained classes and image-level labels of large ...
[19] , one-shot learning [13] and semi-supervised classification [4] . ...
doi:10.1109/iccv.2019.00990
dblp:conf/iccv/YangWC19
fatcat:euiavla6uvfs3id5sobuul6v5y
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