Graph embedding: a general framework for dimensionality reduction

By Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zhang. Computer Vision and Pattern Recognition, 2005

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bc3c2d97e967e18e1eafd9f2b1b887bf79c9d545

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Lei Zhang
3. Yi Yang
4. Luc Van Gool
5. Shuicheng Yan
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Trevor Darrell

In Signal Processing

1. Dacheng Tao
2. Weihong Deng
3. David Dapeng Zhang
4. Thomas S. Huang
5. Shuicheng Yan
6. Yi Yang
7. Feiping Nie
8. Lei Zhang

Citations in the corpus

• Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, background): “[33] proposed a general dimensionality reduction framework called graph embedding.”
• Discriminant Locally Linear Embedding With High-Order Tensor Data (2008, methodology): “As shown in the embedding framework [49], (2) can be resolved by converting it into the following ratio formulation:”
• Nonlinear Dimensionality Reduction with Local Spline Embedding (2009, background): “Analyses and interpretations about these algorithms are given in view of graph embedding [56], kernelization [22], global versus local framework [10], and so on.”
• Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, methodology): “MFA considers both the local geometry of samples in each class and the discriminative information of marginal samples in different classes [29].”
• Trace Ratio vs. Ratio Trace for Dimensionality Reduction (2007, methodology): “Kernel-based LDA and MFA are used as examples for the trace ratio and ratio trace formulations.”
• Spectral Regression for Efficient Regularized Subspace Learning (2007, background): “Despite the different motivations of these algorithms, they can be nicely interpreted in a general graph embedding framework [3, 13, 23].”
• Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation (2010, methodology): “Although the motivation of manifold learning algorithm differs from one to another, the objective function of ISOMap, LLE and LE can be uniformly formulated as follows (Yan et al. 2005).”
• LASAGNE: Locality and Structure Aware Graph Node Embedding (2017, neutral): “ss labels [20]. The learning algorithm alternates between prediction of nodes in neighborhood as in DeepWalk and prediction of nodes having the same labels. Further related approaches can be found in [21, 22, 23, 24, 25, 26]. 2.2 Embedding Words with Word2vec Word2vec [27, 28] is a framework for lea”
• Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, background): “Ç”
• Euclidean Embedding of Co-occurrence Data (2004, methodology): “Such an approach has been previously used to extend embedding methods such as LLE to unseen points (Zhang, 2007).”