@inproceedings{78f6006a00ef40cb81c7d30d99abe440,
title = "KPCA-based node selection for fast KMSE",
abstract = "In this paper we first show that the kernel minimum squared error model is not computationally efficient in feature extraction. To speed up the feature extraction, we linearly express the feature extractor using nodes, i.e. a portion of the training samples in the kernel space. For node selection from the training set, we define two criteria based on Kernel principal component analysis. The nodes are representative and not similar to each other. The experimental results show the feasibility of the proposed method.",
keywords = "Feature Extraction, Kernel Minimum Squared Error, Minimum Squared Error, Pattern Recognition",
author = "Jinghua Wang and Jane You and Qin Li",
note = "Publisher Copyright: {\textcopyright} 2014 CSREA Press.; 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014, at WORLDCOMP 2014 ; Conference date: 21-07-2014 Through 24-07-2014",
year = "2014",
language = "英语",
series = "Proceedings of the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014",
publisher = "CSREA Press",
pages = "165--169",
editor = "Arabnia, \{Hamid R.\} and Leonidas Deligiannidis and Joan Lu and Tinetti, \{Fernando G.\} and Jane You and George Jandieri and Gerald Schaefer and Solo, \{Ashu M. G.\}",
booktitle = "Proceedings of the 2014 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2014",
}