Semi-Supervised Representation Learning based on Probabilistic Labeling
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Summary
A bound on the performance of the algorithm can be used to determine the effectiveness of using the unlabeled data in the algorithm and a kernelized version is presented, which allows non-linear transformations and provides more flexibility in finding the appropriate mapping.
- Type
- preprint
- Published
- 2016-05-10
- Cited by
- 4
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W2364913443
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16157719
Keywords
Probabilistic logic, Artificial intelligence, Representation (politics), Computer science, Machine learning
References
- Semi-Supervised Classification by Low Density Separation
- Learning from Labeled and Unlabeled Data using Graph Mincuts
- Learning from labeled and unlabeled data with label propagation
- Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
- Label propagation through sparse neighborhood and its applications
- Graph Based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood
- Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data
- Discriminative K-SVD for dictionary learning in face recognition
- An improved bound on the finite-sample risk of the nearest neighbor rule
- Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds
- The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
- Nonlinear dimensionality reduction by locally linear embedding.
- Learning discriminative dictionaries with partially labeled data
- Semi-Supervised Self-Training of Object Detection Models
- A semi-supervised formulation to binary kernel spectral clustering
- Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
- Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
- Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions
- A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
- RCV1: A New Benchmark Collection for Text Categorization Research
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