Name
Abstract | ||
|---|---|---|
This paper proposes a novel non-parametric method to robustly embed conditional and posterior distributions to reproducing Kernel Hilbert space (RKHS). Robust embedding is obtained by the eigenvalue decomposition in the RKHS. By retaining only the leading eigenvectors, the noise in data is methodically disregarded. The non-parametric conditional and posterior distribution embedding obtained by our method can be applied to a wide range of Bayesian inference problems. In this paper, we apply it to heterogeneous face recognition and zero-shot object recognition problems. Experimental validation shows that our method produces better results than the comparative algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLA.2016.0016 | 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) |
Keywords | Field | DocType |
Bayesian inference,zero-shot learning,Kernel methods | Kernel (linear algebra),Bayesian inference,Embedding,Pattern recognition,Kernel embedding of distributions,Computer science,Robustness (computer science),Posterior probability,Artificial intelligence,Reproducing kernel Hilbert space,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
978-1-5090-6168-6 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Junaid Nawaz | 1 | 37 | 11.30 |
| Omar Arif | 2 | 22 | 5.87 |