Semi-supervised Learning by Sparse Representation
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Summary
This paper proposes a semi-supervised learning framework based on `1 graph to utilize both labeled and unlabeled data for inference on a graph and demonstrates the superiority of this framework over the counterparts based on traditional graphs.
- Type
- article
- Published
- 2009-04-30
- Cited by
- 284
- References
- 21
- OpenAlex
- https://openalex.org/W38891395
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14697192
Keywords
Computer science, Graph, Artificial intelligence, Semi-supervised learning, Sparse approximation
References
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- Sparse coding with an overcomplete basis set: a strategy employed by V1?
- Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions
- Robust Face Recognition via Sparse Representation
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
- Overview of the face recognition grand challenge
- Principal Component Analysis
- Learning with Local and Global Consistency
- Semi-supervised Discriminant Analysis
- Semi-Supervised Learning Based on Semiparametric Regularization
- Principal Component Analysis (PCA)
- Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
- Semi-supervised learning using Gaussian fields and harmonic functions
- On Transductive Regression
- Regularization and Semi-supervised Learning on Large Graphs
Cited by
- A discrete graph Laplacian for signal processing
- Semantic Graph Construction for Weakly-Supervised Image Parsing
- Adaptive graph construction using data self-representativeness for pattern classification
- A nearest neighbor classifier based on virtual test samples for face recognition
- Label propagation based on collaborative representation for face recognition
- Towards robust subspace recovery via sparsity-constrained latent low-rank representation
- Learning Discriminative Feature Representations for Visual Categorization
- Video-to-text information fusion evaluation for level 5 user refinement
- Semi-supervised learning for image classification
- Image classification by visual bag-of-words refinement and reduction
- Beyond L2-loss functions for learning sparse models
- Automatic Subspace Learning via Principal Coefficients Embedding
- Out-of-Sample Generalizations for Supervised Manifold Learning for Classification
- Semantic annotation and reasoning for sensor data streams
- A matching pursuit based similarity measure for face recognition
- Part-Level Regularized Semi-Nonnegative Coding for Semi-Supervised Learning
- Graph Regularized Sparsity Discriminant Analysis for face recognition
- Image-Based Three-Dimensional Human Pose Recovery by Multiview Locality-Sensitive Sparse Retrieval
- Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features
- Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
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