Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

By Shenghua Gao, Ivor Wai-Hung Tsang, Liang-Tien Chia. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

369 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1e9396a24a88544bbd5460c43d6c4a4e793edd02

How researchers see this paper

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

In Artificial Intelligence

1. Dacheng Tao
2. Lei Zhang
3. Julien Mairal
4. Francis R. Bach
5. Jean Ponce
6. Jun Yu
7. Wangmeng Zuo
8. Peter Richtárik

In Computational Mechanics

1. Lei Zhang
2. Julien Mairal
3. Wangmeng Zuo
4. Francis R. Bach
5. Jean Ponce
6. Dacheng Tao
7. Ivor Wai-Hung Tsang
8. Shenghua Gao

In Computer Vision and Pattern Recognition

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

Citations in the corpus

• Region-Based Saliency Detection and Its Application in Object Recognition (2014, background): “Based on sparse coding, some other sparse coding related feature coding methods [29], [63], [65] are also proposed and achieve good performance for image representation.”
• Deep learning for visual understanding (2017, neutral): “[137] further raised a Hyper-graph Laplacian Sparse Coding (HLSC) method, which extends the LSC to the case where the similarity among the instances is de ned by a hyper graph.”
• Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification (2015, methodology): “Following [16], we define the diagonal degree matrix U with Uii = ∑ j Sij .”
• Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (neutral): “For a long time, the hypergraph relations have often been transformed into another normal graph that is easier to handle [17], [34], [52].”
• Click Prediction for Web Image Reranking Using Multimodal Sparse Coding (2014, methodology): “Laplacian sparse coding (LSC) [19], in which a locality-preserving Laplacian term is added to the sparse code, makes the sparse codes more discriminative while maintaining the similarity of features, and enhancing the sparse coding’s robustness.”
• Sparse Modeling for Image and Vision Processing (2014, background): “In the noiseless case, earlier results have been also obtained Gribonval and Schnass [2010], Geng et al. [2011], and recently it has been shown that under additional assumptions, a good initializer could be found so that the previous type of local consistency results can be applied [Agarwal et al.”