@inproceedings{c16b6ddfae994b3a9682b7750902f05f,
title = "Refining pre-image via error compensation for KPCA-based pattern de-noising",
abstract = "Finding pre-image is crucial for kernel principal component analysis (KPCA) based pattern de-noising. This paper proposes to learn the systematic error of some classical methods of pre-image finding, and to refine the obtained pre-image via error compensation. Experiments based on simulated data as well as real-world data demonstrate that the proposed approach can improve effectively the results from two classical pre-image methods: gradient decent and distance constraint.",
keywords = "Error compensation, Kernel principal component analysis (KPCA), Pre-image, Systematic error",
author = "Jianwu Li and Qiang Tu and Ziye Yan",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1109/ICPR.2016.7899669",
language = "English",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "414--419",
booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR 2016",
address = "United States",
}