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• W2273787639 abstract "Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method." @default.
• W2273787639 created "2016-06-24" @default.
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• W2273787639 date "2016-04-01" @default.
• W2273787639 modified "2023-10-16" @default.
• W2273787639 title "Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning" @default.
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• W2273787639 doi "https://doi.org/10.1016/j.jvcir.2016.01.007" @default.
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