Geometry preserving multi-task metric learning

By Peipei Yang, Kaizhu Huang, Chenglin Liu. 2012

47 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Computational Mechanics

1. Qiang Yang
2. Yu Zhang
3. Amaury Habrard
4. Aurélien Bellet
5. Marc Sebban
6. Michaël Perrot

In Computer Vision and Pattern Recognition

1. Amaury Habrard
2. Qiang Yang
3. Aurélien Bellet
4. Yu Zhang
5. Marc Sebban
6. Mohammad Rostami
7. Gabriella Csurka
8. Wen Li

Citations in the corpus

• A Survey on Multi-Task Learning (2017, background): “For example, multi-task metric learning [74], [113] transforms into an imbalanced binary classification problem where a pair of data points from the same class is treated as positive and that from different classes is negative.”
• A Survey on Metric Learning for Feature Vectors and Structured Data (2013, background): “ance for different values of r. It is shown to outperform mt-LMNN, but the setup is a bit unfair to mt-LMNN since it is forced to be low-rank by eigenvalue thresholding. GPML (Yang et al.) The work of Yang et al. (2012) identifies two drawbacks of previous multi-task metric learning approaches: (i) ML”
• Online Multitask Relative Similarity Learning (2017, unknown): “Based on similar idea, Yang et al. [Yang et al. , 2013] proposed to learn multiple metrics by using von Neumann divergence among metrics.”