Kernel PCA for novelty detection
Kernel PCA for novelty detection
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DOI：
10.1016/j.patcog.2006.07.009
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发表时间：
2007-03-01
影响因子：
8
通讯作者：
Hoffmann, Heiko
中科院分区：
文献类型：
--
作者：
Hoffmann, Heiko
核主成分分析（Kernel Principal Component Analysis，KICA）是PCA的一种非线性扩展。本研究介绍并探讨使用核PCA的新奇检测。训练数据被映射到一个无限维的特征空间。在这个空间中，核PCA提取数据分布的主成分。到相应主子空间的平方距离是新奇的度量。这种新方法在二维合成分布和两个真实世界的数据集上表现出竞争力：手写数字和乳腺癌细胞学。(c)2006模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
Kernel principal component analysis (kernel PICA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on two-dimensional synthetic distributions and on two real-world data sets: handwritten digits and breast-cancer cytology. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.