Spectral regression: a unified subspace learning framework for content-based image retrieval

By Deng Cai, Xiaofei He, Jiawei Han. 2007

102 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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. Xuelong Li
2. Feiping Nie
3. Jiawei Han
4. Deng Cai
5. Dacheng Tao
6. Wei Zhu
7. Xiaofei He
8. Kevin Chen-Chuan Chang

In Media Technology

1. Xuelong Li
2. Feiping Nie
3. Wei Zhu
4. Xiaofei He
5. Deng Cai
6. Jiawei Han
7. Kevin Chen-Chuan Chang
8. Vincent Wenchen Zheng

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “[2] prove that regularized LPP [97] is equivalent to regularized SR [99] if W is symmetric, doubly”
• Unsupervised Large Graph Embedding (2017, methodology): “…include Laplacian Eigenmap (LE) (Belkin and Niyogi 2001), Locality Preserving Projections (LPP) (He and Niyogi 2003), Spectral Regression (SR) (Cai, He, and Han 2007), etc. Spectral based methods always construct similarity matrix firstly, and then perform spectral analysis on the obtained…”
• Graph Learning: A Survey (2021, unknown): “An adjacency graph and a labelled graph were constructed in [78].”
• SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis (2008, background): “Please see [4], [5], [6] for more details.”
• Semi-supervised Discriminant Analysis (2007, methodology): “We can also use the spectral regression technique to solve this singularity problem, please see [5] for details.”