Name
Abstract | ||
|---|---|---|
We consider a similarity-score based paradigm to address scenarios where either the class labels are only partially revealed during learning, or the training and testing data are drawn from heterogeneous sources. The learning problem is subsequently formulated as optimization over a bilinear form of fixed rank. Our paradigm bears similarity to metric learning, where the major difference lies in its aim of learning a rectangular similarity matrix, instead of a proper metric. We tackle this problem in a Riemannian optimization framework. In particular, we consider its applications in pairwise-based action recognition, and cross-domain image-based object recognition. In both applications, the proposed algorithm produces competitive performance on respective benchmark datasets. |
Year | Venue | Field |
|---|---|---|
2013 | ICML | Similarity learning,Pairwise comparison,Bilinear form,Pattern recognition,Computer science,Action recognition,Riemannian optimization,Artificial intelligence,Test data,Machine learning,Similarity matrix,Cognitive neuroscience of visual object recognition |
DocType | Citations | PageRank |
Conference | 3 | 0.37 |
References | Authors | |
30 | 1 | |