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Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these...
In order to preserve the local structure of the input data, many dimensionality reduction algorithms are involved in the definition of certain locality in the feature space. Thus local patch selection will directly affect the performance of these algorithms. In this paper, we propose a new method of local patch selection for dimensionality reduction. Specially, we incorporate our method into locality-preserving...
Multiple kernel learning (MKL) usually searches for linear (nonlinear) combinations of predefined kernels by optimizing some performance measures. However, previous MKL algorithms cannot deal with Lq norm MKL if q
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