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A Review article on Semi- Supervised Clustering Framework for High Dimensional Data
2019
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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Clustering algorithms are based on active learning, with ensemble clustering-means algorithm, data streams with flock, fuzzy clustering for shape annotations, Incremental semi supervised clustering, Weakly ...
supervised clustering, with minimum labeled data, self-organizing based on neural networks. ...
[6] unifies the first two methods under a general probabilistic framework. However, most existing semi-supervised methods are not designed for handling high-dimensional data. ...
doi:10.32628/cseit195410
fatcat:xl37f2eb6bagjfwdscc7fwi2p4
A novel semi-supervised multi-view clustering framework for screening Parkinson's disease
[article]
2020
arXiv
pre-print
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In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology ...
Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. ...
Acknowledgments We would like to thank the Parkinson's Progression Markers Initiative (PPMI) for the datasets used in our experiments. ...
arXiv:2003.04760v1
fatcat:ed3m3x2cpnc23nl7ksswccn7ku
Semi-Supervised Linear Discriminant Clustering
2014
IEEE Transactions on Cybernetics
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This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). ...
The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K-means and linear discriminant analysis (LDA). ...
Related Work
A. Semi-supervised Learning Semi-supervised learning methods can be further classified into semi-supervised classification and semi-supervised clustering methods. ...
doi:10.1109/tcyb.2013.2278466
pmid:23996591
fatcat:dpxxp6lcyraxhb2rzrbryy2pqa
A General Model for Semi-Supervised Dimensionality Reduction
2012
Procedia Engineering
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This paper focuses on semi-supervised dimensionality reduction. In this scenario, we present a general model for semi-supervised dimensionality reduction with pairwise constraints (SSPC). ...
Experimental results on a collection of benchmark data sets show that SSPC is superior to many established dimensionality reduction methods. ...
Other semi-supervised DR methods based on pairwise constraints are related to semi-supervised clustering [8] [9] [10] . ...
doi:10.1016/j.proeng.2012.01.529
fatcat:f32o37juxzbljhoa2t7joeayi4
Enhancing semi-supervised clustering
2007
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07
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This paper thus fills this crucial void by developing a Semi-supervised Clustering method based on spheRical K-mEans via fEature projectioN (SCREEN). ...
Despite the vast amount of expert knowledge spent on this problem, most existing work is not designed for handling high-dimensional sparse data. ...
Xingquan Zhu at Florida Atlantic University for his insightful comments. ...
doi:10.1145/1281192.1281268
dblp:conf/kdd/TangWXZ07
fatcat:co3326z5l5dfbpvfjq23cshxvi
Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans
2013
Fuzzy sets and systems (Print)
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While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. ...
The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. ...
Percentage of
Semi-supervised classification
Semi-supervised clustering
labeled examples
Dimensionality reduction Without dimensionality reduction Dimensionality reduction Without dimensionality reduction ...
doi:10.1016/j.fss.2013.01.004
fatcat:5qgbucjr4fcxvhffxdgdqbi4gq
An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques
2022
Journal of Spectral Imaging
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As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. ...
Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches. ...
Classification of images can be categorised as supervised, semi-supervised and cluster analysis based on the use of training samples. ...
doi:10.1255/jsi.2022.a1
fatcat:rue5klkmlfcrzftepc6lzfcbfe
Semi-supervised annotation of brushwork in paintings domain using serial combinations of multiple experts
2006
Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06
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Each expert focuses on the annotation of the currently available samples from its unlabeled pool using semi-supervised agglomerative clustering. ...
In particular, we employ the serial multi-expert framework with semi-supervised clustering methods to perform the annotation of brushwork patterns. ...
Existing methods for semi-supervised clustering fall into two general categories: constraint-based and distance-based. ...
doi:10.1145/1180639.1180752
dblp:conf/mm/YelizavetaCJ06a
fatcat:biglhz2irrg77csy365ubr2zga
Semi-Supervised Clustering With Multiresolution Autoencoders
2018
2018 International Joint Conference on Neural Networks (IJCNN)
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The proposed strategy is evaluated on a set of real-world benchmarks also in comparison with well-known state-of-the-art semi-supervised clustering methods. ...
Successively, the network models are employed to supply a new embedding representation on which clustering is performed. ...
The authors acknowledge the support of the National Research Agency within the framework of the program "Investissements d'Avenir" for the GEOSUD project (ANR-10-EQPX-20). ...
doi:10.1109/ijcnn.2018.8489353
dblp:conf/ijcnn/IencoP18
fatcat:yjqx44c4bngezmyg32zxpid22u
FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. ...
Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. ...
The KNN classifier as it tries to find the nearest data points based on their classification and the SVM is a learning algorithm that analyses data for classification in supervised learning methods. ...
doi:10.35940/ijitee.b6907.129219
fatcat:3wycvghewngcbbpprtbmvaim4i
Constraint Selection-Based Semi-supervised Feature Selection
2011
2011 IEEE 11th International Conference on Data Mining
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Dimensionality reduction is a significant task when dealing with high-dimensional data, this reduction can be done by feature selection, which means to select the most appropriate features for data analysis ...
In this paper, we present a novel feature selection approach based on an efficient selection of pairwise constraints. ...
CONCLUSION In this paper, we proposed a framework for feature selection based on constraint selection for semi-supervised dimensionality reduction. ...
doi:10.1109/icdm.2011.42
dblp:conf/icdm/HindawiAB11
fatcat:wtgunbsdijgujd6i4diri2hrjy
IEEE Access Special Section Editorial: Data Mining and Granular Computing in Big Data and Knowledge Processing
2019
IEEE Access
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Zhang et al, in the article entitled ''CPCA: A feature semantics based crowd dimension reduction framework,'' proposed a crowd-based dimension reduction framework called Crowd Principal Component Analysis ...
To deal with a large number of medial datasets collected from IoT-based platform, Yang et al, in the article entitled ''GAN-based semi-supervised learning approach for clinical decision support in health-IoT ...
doi:10.1109/access.2019.2908776
fatcat:7km2edtcuzeutnwy3pjbvg264e
Unsupervised User Stance Detection on Twitter
[article]
2020
arXiv
pre-print
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Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. ...
Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality ...
Most recent work on stance detection has focused on supervised or semi-supervised classification. Copyright c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). ...
arXiv:1904.02000v3
fatcat:wzemkgzz6fakrkp6e6rmuiyjva
AutoEmbedder: A semi-supervised DNN embedding system for clustering
2020
Knowledge-Based Systems
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2020 pre-print arXiv:2007.05830v1 fatcat:cxxiytfclbha7hbzxebb5wh7zq
The AutoEmbedder outperforms most of the existing DNN based semi-supervised methods tested on famous datasets. ...
Deep clustering method downsamples high dimensional data, which may also relate clustering loss. Deep clustering is also introduced in semi-supervised learning (SSL). ...
Most semi-supervised and unsupervised learning architecture relates AE for data dimensionality reduction. ...
doi:10.1016/j.knosys.2020.106190
fatcat:errpsdprz5fqbhe2u4nfl56z44
Unsupervised User Stance Detection on Twitter
2020
Proceedings of the ... International AAAI Conference on Weblogs and Social Media
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Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. ...
Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality ...
Most recent work on stance detection has focused on supervised or semi-supervised classification. ...
doi:10.1609/icwsm.v14i1.7286
fatcat:25xf7pjnkrastdwl47bufrrtpq
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