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This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. We exploit an orthogonal projection based algorithm to select samples from large-size ones; meanwhile, we propose an algorithm based on the orthogonal complementary subspace...
When the desired signal is present in the training snapshots, the performance of conventional orthogonal projection (OP) adaptive beamforming degrades severely due to the desired signal cancellation effect. To overcome this deficiency, the improved orthogonal projection (IOP) adaptive beamforming by using reconstructed interference covariance matrix is proposed. In the proposed algorithm, the interference...
Theoretical analyses on generalization error of a model space for kernel regressors with respect to training samples are given in this paper. In general, the distance between an unknown true function and a model space tends to be small with a larger set of training samples. However, it is not clarified that a larger set of training samples achieves a smaller difference at each point of the unknown...
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