Learning a tree of metrics with disjoint visual features
We introduce an approach to learn discriminative visual representations while exploiting
external semantic knowledge about object category relationships. Given a hierarchical …
external semantic knowledge about object category relationships. Given a hierarchical …
Learning a mixture of sparse distance metrics for classification and dimensionality reduction
This paper extends the neighborhood components analysis method (NCA) to learning a
mixture of sparse distance metrics for classification and dimensionality reduction. We …
mixture of sparse distance metrics for classification and dimensionality reduction. We …
Hierarchical distance metric learning for large margin nearest neighbor classification
S Sun, Q Chen - International Journal of Pattern Recognition and …, 2011 - World Scientific
Distance metric learning is a powerful tool to improve performance in classification,
clustering and regression tasks. Many techniques have been proposed for distance metric …
clustering and regression tasks. Many techniques have been proposed for distance metric …
Low rank metric learning with manifold regularization
In this paper, we present a semi-supervised method to learn a low rank Mahalanobis
distance function. Based on an approximation to the projection distance from a manifold, we …
distance function. Based on an approximation to the projection distance from a manifold, we …
SERAPH: Semi-supervised metric learning paradigm with hyper sparsity
We propose a general information-theoretic approach called Seraph (SEmi-supervised
metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon …
metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon …
Sparse kernel regression for traffic flow forecasting
R Huang, S Sun, Y Liu - Advances in Neural Networks–ISNN 2011: 8th …, 2011 - Springer
In this paper, a new kernel regression algorithm with sparse distance metric is proposed and
applied to the traffic flow forecasting. The sparse kernel regression model is established by …
applied to the traffic flow forecasting. The sparse kernel regression model is established by …