Two-dimensional margin, similarity and variation embedding
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Summary
In this work, two adjacency graphs are constructed to model the margin and information including similarity and variation of face images from the same class, respectively, and then incorporate the information and margin into the dimensionality reduction function.
- Type
- article
- Published
- 2012-06-01
- Cited by
- 17
- References
- 15
- OpenAlex
- https://openalex.org/W75788093
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46324095
Keywords
Pattern recognition (psychology), Discriminant, Facial recognition system, Artificial intelligence, Dimensionality reduction
References
- Exploiting Known Taxonomies in Learning Overlapping Concepts
- Enhanced Fisher linear discriminant models for face recognition
- Sequential row-column independent component analysis for face recognition
- Two-dimensional supervised local similarity and diversity projection
- Fuzzy two-dimensional local graph embedding discriminant analysis (F2DLGEDA) with its application to face and palm biometrics
- Two-Dimensional Linear Discriminant Analysis
- Two-dimensional PCA: a new approach to appearance-based face representation and recognition
- One improvement to two-dimensional locality preserving projection method for use with face recognition
- Local Fisher discriminant analysis for supervised dimensionality reduction
- Face recognition using Laplacianfaces
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
- Sparse two-dimensional local discriminant projections for feature extraction
- Two-Dimensional Maximum Margin Feature Extraction for Face Recognition
- Local discriminant embedding and its variants
- Discriminant Analysis on Embedded Manifold
- Two-dimensional local graph embedding discriminant analysis (2DLGEDA) with its application to face and palm biometrics
Cited by
- Nonparametric subspace analysis fused to 2DPCA for face recognition
- Feature extraction using two-dimensional neighborhood margin and variation embedding
- Joint geometry and variability for image recognition
- Feature extraction using graph discriminant embedding
- Joint Global and Local Structure Discriminant Analysis
- Discriminant similarity and variance preserving projection for feature extraction
- Incorporating local and global geometric structure for hyperspectral image classification
- Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification
- A novel matrix-based method for face recognition
- Discriminant structure embedding for image recognition
- Parameterless reconstructive discriminant analysis for feature extraction
- Laplacian Maximum Margin Criterion for Image Recognition
- Discriminant Neighborhood Structure Embedding Using Trace Ratio Criterion for Image Recognition
- Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams
- Learning more distinctive representation by enhanced PCA network
- Feature extraction based on graph discriminant embedding and its applications to face recognition
- Joint Global and Local Structure Discriminant Analysis
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