Manifold Discriminant Analysis

By Ruiping Wang, Xilin Chen. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

308 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/16a3f87d9956c82c9e04d35fcf006e404297a6aa

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. Jiwen Lu
2. Luc Van Gool
3. Xilin Chen
4. Jie Zhou
5. Shiguang Shan
6. Weihong Deng
7. G. Wang
8. Zhiwu Huang

In Media Technology

1. Jiwen Lu
2. Shiguang Shan
3. Zhiwu Huang
4. Xilin Chen
5. Ruiping Wang
6. Jie Zhou
7. Yap-Peng Tan
8. G. Wang

Citations in the corpus

• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “Different from existing manifold distance methods [29, 30], which calculate the nearest subspace-subspace distance to approximate the manifold-manifold distance, we propose here a novel reconstruction-based method to compute the manifold distance by using all rather than selected samples in the two…”
• Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition (2014, methodology): “Inspired by [28], while only considering the “margin” among corresponding expressionlets, we exploit a graph-embedding [31] method by constructing partially connected graphs to keep the links between expressionlets with the same semantics.”
• Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (2015, methodology): “Over the years, a surge of methods (Wang et al., 2008; Wang & Chen, 2009; Cevikalp & Triggs, 2010; Hu et al., Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015.”
• Attention-Aware Deep Reinforcement Learning for Video Face Recognition (2017, unknown): “MDA [37] 67.”
• Multi-manifold deep metric learning for image set classification (2015, methodology): “…(MSM) [38], Discriminant Canonical Correlation analysis (DCC) [16], Manifold-to-Manifold Distance (MMD) [36], Manifold Discriminant Analysis (MDA) [34], Affine Hull based Image Set Distance (AHISD) [2], Convex Hull based Image Set Distance (CHISD) [2], Sparse Approximated Near-est Point (SANP)…”
• Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition (2015, methodology): “On this dataset, there are several state-of-the-art methods [39, 37, 38, 29, 17, 20] with other kinds of set modeling or other kinds of classifiers.”
• Covariance discriminative learning: A natural and efficient approach to image set classification (2012, methodology): “To allow comparison with the literature we followed the same protocol as [5], [10], [26], [27].”