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Diversity and accuracy are the two key factors that decide the ensemble generalization error. Constructing a good ensemble method by balancing these two factors is difficult, because increasing diversity is at the cost of reducing accuracy normally. In order to improve the performance of an ensemble while avoiding the difficulty derived of balancing(More)
Decreasing the individual error and increasing the diversity among classifiers are two crucial factors for improving ensemble performances. Nevertheless, the ‘‘kappa-error’’ diagram shows that enhancing the diversity is at the expense of reducing individual accuracy. Hence, a newmethod namedMatching Pursuit Optimization Ensemble Classifiers (MPOEC) is(More)
Recently the underlying sparse representation structure in high dimensional data has received considerable attention in pattern recognition and computer vision. In this paper, we propose a novel semi-supervised dimensionality reduction (SDR) method, named Double Linear Regressions (DLR), to tackle the Single Labeled Image per Person (SLIP) face recognition(More)
Riemannian optimization has been widely used to deal with the fixed low-rank matrix completion problem, and Riemannian metric is a crucial factor of obtaining the search direction in Riemannian optimization. This paper proposes a new Riemannian metric via simultaneously considering the Riemannian geometry structure and the scaling information, which is(More)
It is very expensive and time-consuming to annotate huge amounts of data. Active learning would be a suitable approach to minimize the effort of annotation. A novel active learning approach, coupled K nearest neighbor pseudo pruning (CKNNPP), is proposed in the paper, which is based on querying examples by KNNPP method. The KNNPP method applies k nearest(More)
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