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A Self-Supervised Framework for Clustering Ensemble [chapter]

Liang Du, Yi-Dong Shen, Zhiyong Shen, Jianying Wang, Zhiwu Xu
2013 Lecture Notes in Computer Science  
In this paper, we propose a novel self-supervised learning framework for clustering ensemble.  ...  Clustering ensemble refers to combine a number of base clusterings for a particular data set into a consensus clustering solution.  ...  We would like to thank all anonymous reviewers for their helpful comments. This work is supported in part by NSFC grant 60970045 and China National 973 project 2013CB329305.  ... 
doi:10.1007/978-3-642-38562-9_26 fatcat:rnrhy5iprrayhatmnelegp2tcm

Epithelium and Stroma Identification in Histopathological Images Using Unsupervised and Semi-Supervised Superpixel-Based Segmentation

Shereen Fouad, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini
2017 Journal of Imaging  
Consensus Clustering (CC) Frameworks The CC framework exploited here involves three main steps (a) creation of an ensemble of multiple cluster solutions; (b) selection of an effective sub-set of cluster  ...  Assign the new labels in I MG to partition P Self-Training Semi-Supervised Classification Based on Consensus Clustering This section introduces a semi-supervised self-training classifier based on the  ... 
doi:10.3390/jimaging3040061 fatcat:xgidtqeydfhdjl4sbdcguywnwe

Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles [article]

Bram Wallace, Devansh Arpit, Huan Wang, Caiming Xiong
2021 arXiv   pre-print
In this work, we provide a framework to perform self-supervised model ensembling via a novel method of learning representations directly through gradient descent at inference time.  ...  Meanwhile, model ensembling is one of the most universally applicable techniques in supervised learning literature and practice, offering a simple solution to reliably improve performance.  ...  DISCUSSION In this work, we presented a novel self-supervised ensembling framework which learns representations directly through gradient descent.  ... 
arXiv:2110.10293v1 fatcat:kuqlalsw6nbylnnik57eh7f4sm

A Review article on Semi- Supervised Clustering Framework for High Dimensional Data

M. Pavithra, R. M. S. Parvathi
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Incremental semi-supervised clustering ensemble framework (ISSCE) which makes utilization of the advantage of the arbitrary subspace method, the limitation spread approach, the proposed incremental ensemble  ...  supervised clustering, with minimum labeled data, self-organizing based on neural networks.  ...  REFERENCES incremental semi-supervised clustering ensemble framework (ISSCE) is intended to expel the copy ensemble members.  ... 
doi:10.32628/cseit195410 fatcat:xl37f2eb6bagjfwdscc7fwi2p4

Consensus Clustering-Based Undersampling Approach to Imbalanced Learning

Aytuğ Onan
2019 Scientific Programming  
In this paper, we present a consensus clustering based-undersampling approach to imbalanced learning.  ...  In the classification phase, five supervised learning methods (namely, naïve Bayes, logistic regression, support vector machines, random forests, and k-nearest neighbor algorithm) and three ensemble learner  ...  In this paper, a consensus clustering-based framework is presented to identify the informative instances of majority class through the use of a cluster ensemble method.  ... 
doi:10.1155/2019/5901087 fatcat:k2ub4mjelveyvajhdrir4pz724

Self-supervised spectral matching network for hyperspectral target detection [article]

Can Yao, Yuan Yuan, Zhiyu Jiang
2021 arXiv   pre-print
To address these problems, a spectral mixing based self-supervised paradigm is designed for hyperspectral data to obtain an effective feature representation.  ...  The model adopts a spectral similarity based matching network framework.  ...  K is the number of cluster. Self-supervised Representation Learning with Pairbased Loss To make full use of the unlabeled data, we train the model in a self-supervised manner.  ... 
arXiv:2105.04078v1 fatcat:jc5iycj5ofdzrpz6bkzj6hciqi

ISVS3CE: Incremental Support Vector Semi-Supervised Subspace Clustering Ensemble and ENhanced Bat Algorithm (ENBA) for High Dimensional Data Clustering

2019 International journal of recent technology and engineering  
In the recent work, Incremental Soft Subspace Based Semi-Supervised Ensemble Clustering (IS4EC) framework was proposed which helps in detecting clusters in the dataset.  ...  In order to solve these issues of traditional cluster ensemble methods, first propose an Incremental Support vector Semi-Supervised Subspace Clustering Ensemble (ISVS3CE) framework which makes utilized  ...  Proposed Incremental Support vector Semi-Supervised Subspace Clustering Ensemble framework (ISVS3CE) A.  ... 
doi:10.35940/ijrte.b1724.078219 fatcat:3iosqeaodzhvrhbks2hrcaisga

Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering [article]

Tapas Nayak and Navonil Majumder and Soujanya Poria
2021 arXiv   pre-print
In this paper, we propose a self-ensemble filtering mechanism to filter out the noisy samples during the training process.  ...  Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.  ...  , Modelling, and Explainable Reasoning for General Expertise).  ... 
arXiv:2108.09689v1 fatcat:n2zcsrb6mna4pffugkdzytzoie

Active semi-supervised framework with data editing

Xue Zhang, Wang-xin Xiao
2012 2012 International Conference on Systems and Informatics (ICSAI2012)  
A data editing technique is used to identify and remove noise introduced by semi-supervised labeling.  ...  The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data.  ...  These retrieved documents are then exploited by a semi-supervised framework.  ... 
doi:10.1109/icsai.2012.6223045 fatcat:xln6ixpbfjhpblteaxcx3f5pzq

Semi-supervised Learning for Multi-target Regression [chapter]

Jurica Levatić, Michelangelo Ceci, Dragi Kocev, Sašo Džeroski
2015 Lecture Notes in Computer Science  
We use ensembles of predictive clustering trees in a self-training fashion: most reliable predictions on unlabeled data are iteratively used to re-train the model.  ...  Our results provide a proof-of-concept: Unlabeled data improves predictive performance of ensembles for multi-target regression, however further efforts are needed to automatically select the optimal threshold  ...  Self-training for MTR To perform semi-supervised learning with ensembles of PCTs for MTR, we consider a self-training approach.  ... 
doi:10.1007/978-3-319-17876-9_1 fatcat:mpgbnv3wonh37it2v42sc6wclu

Active semi-supervised framework with data editing

Xue Zhang, Wangxin Xiao
2012 Computer Science and Information Systems  
A data editing technique is used to identify and remove noise introduced by semi-supervised labeling.  ...  The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data.  ...  These retrieved documents are then exploited by a semi-supervised framework.  ... 
doi:10.2298/csis120202045z fatcat:cwqvrxbolve43knd2pkwpyj5ne

OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision [article]

Xinyang Zhang, Chenwei Zhang, Xian Li, Xin Luna Dong, Jingbo Shang, Christos Faloutsos, Jiawei Han
2022 arXiv   pre-print
Finally, we discover new attributes and values through the self-ensemble of our framework, which handles the open-world challenge.  ...  We propose a principled framework that first generates attribute value candidates and then groups them into clusters of attributes.  ...  Finally, we discover new attributes and values with self-ensembled based inference. Predictions are used for training in next framework iteration.  ... 
arXiv:2204.13874v1 fatcat:42wtonf4z5fcrhl62csckv76ba

Self-paced ensemble learning for speech and audio classification [article]

Nicolae-Catalin Ristea, Radu Tudor Ionescu
2021 arXiv   pre-print
Instead of just combining the models, we propose a self-paced ensemble learning scheme in which models learn from each other over several iterations.  ...  To demonstrate the generality of our self-paced ensemble learning (SPEL) scheme, we conduct experiments on three audio tasks.  ...  For example, Zhou et al. [7] proposed a novel self-paced clustering ensemble that gradually adds instances, from easy to difficult, into the ensemble learning.  ... 
arXiv:2103.11988v2 fatcat:yuinpabrgvejncw6szrss4eyii

Self-Supervised Learning for Robust Video Indexing

Ralph Ewerth, Bernd Freisleben
2006 2006 IEEE International Conference on Multimedia and Expo  
In this paper, we propose to use a novel self-supervised learning framework for robust video indexing to address this issue.  ...  Finally, a specialized ensemble of classifiers is employed for the given video for decision making.  ...  For 8 out of 12 videos, the self-supervised approach leads to a lower total number of errors (including both false alarms and missed hits) than the supervised ensemble.  ... 
doi:10.1109/icme.2006.262889 dblp:conf/icmcs/EwerthF06 fatcat:purpr6utmbeqnlw2t3sgmzkz3a

Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification [article]

Xiao Zhang, Yixiao Ge, Yu Qiao, Hongsheng Li
2021 arXiv   pre-print
and ensembled pseudo labels.  ...  Unsupervised object re-identification targets at learning discriminative representations for object retrieval without any annotations.  ...  This work is supported in part by Centre for Perceptual and Interactive Intelligence.  ... 
arXiv:2106.06133v2 fatcat:advlj54h3ncorojix6zfositiy
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