Efficient KPCA-Based Feature Extraction: A Novel Algorithm and Experiments
作者： Yong Xu , David Zhang , Jing-Yu Yang , Zhong Jing , Miao Li
DOI: 10.1007/978-3-540-37258-5_23
关键词: Speedup 、 Pattern recognition (psychology) 、 Feature extraction 、 Benchmark (computing) 、 Algorithm 、 Kernel principal component analysis 、 Set (abstract data type) 、 Artificial intelligence 、 Engineering 、 Pattern recognition 、 Feature vector 、 Principal component analysis
摘要: KPCA has been widely used for feature extraction. It is noticeable that the efficiency of KPCA-based extraction in inverse proportion to size training sample set. In order speed up extraction, we develop a novel algorithm(i.e. IKPCA) which improves with distinctive viewpoint. The algorithm methodologically consistent clear physical meaning. Experiments on several benchmark datasets illustrate IKPCA-based much faster than ratio time may be smaller 0.30. Furthermore, classification accuracy corresponding IKPCA comparable KPCA.
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