کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
528837 | 869613 | 2016 | 11 صفحه PDF | دانلود رایگان |
• We propose a kernel propagation strategy (KPS) for out-of-sample projections.
• We develop a specific kernel propagation algorithm based on KPS.
• Extensive experiments have revealed the superior performance of our algorithm.
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.
Journal: Journal of Visual Communication and Image Representation - Volume 36, April 2016, Pages 69–79