Discriminant Locally Linear Embedding With High-Order Tensor Data

By Xuelong Li, Stephen Lin, Shuicheng Yan, Dong Xu. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008

310 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/e0532953dab536c140b4fa05ca3bec5375244098

How researchers see this paper

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

In Biomedical Engineering

1. Dacheng Tao
2. Xuelong Li
3. Tao Mei
4. Feiping Nie
5. Changshui Zhang
6. Yongdong Zhang
7. Dong Xu
8. Yun Raymond Fu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xuelong Li
3. Luc Van Gool
4. Yi Yang
5. Trevor Darrell
6. Ling Shao
7. Xiaogang Wang
8. Feiping Nie

In Human-Computer Interaction

1. Dacheng Tao
2. Xuelong Li
3. Tat-seng Chua
4. Feiping Nie
5. Yonggang Wen
6. Changshui Zhang
7. Dong Xu

Citations in the corpus

• Multiview Spectral Embedding (2010, methodology): “Representative algorithms include Isomap [7], LLE [8], LE [9], Hessian eigenmaps [10], local tangent space alignment [11], transductive component analysis [2], discriminative locality alignment [30], and DLLE [27].”
• Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, background): “Although the performances of traditional unsupervised feature selection approaches are prominent in many cases, their efficiencies can also be improved since (1) from the view of manifold learning [22][23], the high dimensional data are nearly lying on a low dimensional manifold.”
• Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, background): “Almost all existing subspace learning algorithms [2], [11], [12], [20], [40] have been developed by assuming the training and testing samples are i.”
• Gabor-Based Region Covariance Matrices for Face Recognition (2008, background): “Face recognition [23] has been an active research topic over the past few decades due to its scientific challenges and potential applications.”
• Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “Upon obtaining the structure analysis results, the second step is to extract features-consisting mainly of features of the key frames, objects, text, andmotion for further mining [219]–[221].”
• Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction (2010, background): “N PAST decades, a large number of dimension reduction techniques [2], [ 13 ], [25], [29], [30], [35] have been proposed.”