Making metric learning algorithms invariant to transformations using a projection metric on Grassmann manifolds

Author:

Goudarzi Zahra,Adibi Peyman,Grigat Rolf-Rainer,Ehsani Mohammad Saeid

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference40 articles.

1. Absil P-A, Mahony R, Sepulchre R (2009) Optimization algorithms on matrix manifolds. Princeton University Press, Princeton

2. Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a Mahalanobis metric from equivalence constraints. J Mach Learn Res 6:937–965

3. Bi Y, Fan B, Wu F (2015) Beyond Mahalanobis metric: Cayley-Klein metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2339–2347

4. Candemir S, Borovikov E, Santosh KC, Antani S, Thoma G (2015) Rsilc: rotation-and scale-invariant, line-based color-aware descriptor. Image Vis Comput 42:1–12

5. Crammer K, Singer Y (2001) On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res 2(Dec):265–292

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