Back to Search
Start Over
Joint dynamic sparse learning and its application to multi-view face recognition.
- Source :
- Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012); 1/ 1/2012, p1671-1674, 4p
- Publication Year :
- 2012
-
Abstract
- We propose a novel joint dynamic sparsity regularization for joint learning of multiple tasks (i.e., multiple observations of the same physical event by a set of homogeneous or heterogenous sensors). The proposed method not only combines the strength of different tasks but also has the flexibility of selecting a set of different atoms for each task, with a class-wise constraint, which is more flexible and even crucial in many real-world scenarios. We develop an efficient learning algorithm for the joint dynamic sparsity using the accelerated proximal gradient descent. The proposed method is applied to a multi-view face recognition task and the experimental results on the public CMU Multi-PIE dataset verify its effectiveness. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISBNs :
- 9781467322164
- Database :
- Complementary Index
- Journal :
- Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)
- Publication Type :
- Conference
- Accession number :
- 86627670