Semi-Supervised Dimensionality Reduction
By Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen. SDM, 2007
345 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/5a91eabaa633b6b168650629b4ae20dcf3f8de9a
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
- Miloš Radovanović
- Mirjana Ivanovic
- Alexandros Nanopoulos
- Laurenz Wiskott
- Y. Saad
- Effrosini Kokiopoulou
- Alberto N. Escalante
- Jie Chen
In Computational Theory and Mathematics
- Y. Saad
- Effrosini Kokiopoulou
- Miloš Radovanović
- Mirjana Ivanovic
- Alexandros Nanopoulos
- Laurenz Wiskott
- Jie Chen
- Michele Benzi
In Computer Vision and Pattern Recognition
- Jie Chen
- Y. Saad
- Alberto N. Escalante
- Effrosini Kokiopoulou
- Miloš Radovanović
- Mirjana Ivanovic
- Alexandros Nanopoulos
- Laurenz Wiskott
Citations in the corpus
- Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data (2010, background): “, 2005), semi-supervised (Zhang et al., 2007), and unsupervised approaches (van der Maaten et al.”
- How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis (2013, result): “Later, Zhang et al. (2007) proposed semi-supervised dimensionality reduction (SSDR), which is similar to PCGFP and also supports semi-supervised learning.”
- A Survey of Optimization Methods From a Machine Learning Perspective (2019, neutral): “including classification tasks [70], [71]; regression tasks [72]; clustering tasks [73], [74]; and dimensionality reduction tasks [75], [76].”
- Trace optimization and eigenproblems in dimension reduction methods (2011, background): “This criterion, which is closely related to that proposed in [68], makes use of both labeled and unlabeled data.”