In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
We present a new approach to learning image metrics. The main advantage of our method lies in a formulation that requires only a few pairwise examples. Apparently, based on the li...
We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements...
In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between ...
Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu