Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning

By Matthew F. Der, Lawrence K. Saul. 2012

33 citations recorded by the source corpus.

How researchers see this paper

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

1. Julian McAuley
2. Anton van den Hengel
3. Aurélien Bellet
4. Xu Chen
5. Amaury Habrard
6. Han-Jia Ye
7. Mohammad Rostami
8. Yongfeng Zhang

Citations in the corpus

• Image-Based Recommendations on Styles and Substitutes (2015, background): “Similar ideas appear in [4, 35], which also consider the problem of metric learning via low-rank embeddings, albeit using a different objective than the one we consider here.”
• A Survey on Metric Learning for Feature Vectors and Structured Data (2013, background): “Alternative ways of solving the problem have been proposed (Torresani and Lee, 2006; Nguyen and Guo, 2008; Park et al., 2011; Der and Saul, 2012).”
• Linear dimensionality reduction: survey, insights, and generalizations (2014, methodology): “…et al., 2003), neighborhood component analysis (Goldberger et al., 2004), collapsing classes (Globerson and Roweis, 2005), discriminative component analysis (Peltonen et al., 2007), latent coincidence analysis (Der and Saul, 2012), and an online, large-scale method (Chechik et al., 2009).”