Confidence-weighted safe semi-supervised clustering
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Summary
Confidence-weighted safe semi-supervised clustering where prior knowledge is given in the form of class labels is proposed where the outputs of the labeled samples with high confidences are restricted to be the given prior labels and those of the local homogeneous unlabeled neighbors modeled by the local graph.
- Type
- article
- Published
- 2019-05-01
- Cited by
- 36
- References
- 40
- OpenAlex
- https://openalex.org/W2917681711
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:86408107
Keywords
Cluster analysis, Computer science, Artificial intelligence, Graph, Pattern recognition (psychology)
References
- Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection
- Towards designing risk-based safe Laplacian Regularized Least Squares
- A Semi-supervised Gaussian Mixture Model for Image Segmentation
- A k-means clustering algorithm
- Partially supervised clustering for image segmentation
- Semi-supervised hybrid clustering by integrating Gaussian mixture model and distance metric learning
- Data clustering: 50 years beyond K-means
- Using clustering analysis to improve semi-supervised classification
- Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering
- Towards Making Unlabeled Data Never Hurt
- Manifold regularized semi-supervised Gaussian mixture model.
- Discriminative structure selection method of Gaussian Mixture Models with its application to handwritten digit recognition
- Safety-Aware Semi-Supervised Classification
- Spectral clustering: A semi-supervised approach
- Semi-supervised clustering with metric learning: An adaptive kernel method
- Research of semi-supervised spectral clustering algorithm based on pairwise constraints
- Semi-supervised fuzzy clustering: A kernel-based approach
- Semi-supervised fuzzy clustering with metric learning and entropy regularization
- Integrating constraints and metric learning in semi-supervised clustering
- Fuzzy clustering with partial supervision
Cited by
- An effective framework based on local cores for self-labeled semi-supervised classification
- Industrial Security Solution for Virtual Reality
- Deep-Learning-Enabled Security Issues in the Internet of Things
- A semi-supervised self-training method based on density peaks and natural neighbors
- Joint exploring of risky labeled and unlabeled samples for safe semi-supervised clustering
- A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients
- TS3FCM: trusted safe semi-supervised fuzzy clustering method for data partition with high confidence
- A New Approach for Semi-supervised Fuzzy Clustering with Multiple Fuzzifiers
- Adaptive safety-aware semi-supervised clustering
- AN IMPROVEMENT OF TRUSTED SAFE SEMI-SUPERVISED FUZZY CLUSTERING METHOD WITH MULTIPLE FUZZIFIERS
- Application of secure semi-supervised fuzzy clustering in object detection from remote sensing images
- Semi-supervised fuzzy clustering algorithm based on prior membership degree matrix with expert preference
- A review on semi-supervised clustering
- Safe semi-supervised clustering based on Dempster-Shafer evidence theory
- PLAHS: A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process
- Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data
- Neighborhood information based semi-supervised fuzzy C-means employing feature-weight and cluster-weight learning
- Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering
- Discrimination-aware safe semi-supervised clustering
- A robust self-training algorithm based on relative node graph
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