Subset Basis Approximation of Kernel Principal Component Analysis
Subset Basis Approximation of Kernel Principal Component Analysis
复制标题
DOI：
10.5772/37051
复制
发表时间：
2012-03
影响因子：
10.6
通讯作者：
Y. Washizawa
中科院分区：
文献类型：
--
作者：
Y. Washizawa
主成分分析(PCA)因其定义简单而得到了广泛的应用。特别是，主成分分析的非线性推广已经被提出并在不同的领域中得到应用。主成分分析的非线性推广，如主曲线(Hastie&Stuetzle，1989)和流形(Gorban et al.，2008)，与其他非线性维度技术如ISOMAP(Tenenbaum et al.，2000)和局部线性嵌入(LLE)(Roweis&Saul，2000)相比，具有直观的解释和公式。
Principal component analysis (PCA) has been extended to various ways because of its simple definition. Especially, non-linear generalizations of PCA have been proposed and used in various areas. Non-linear generalizations of PCA, such as principal curves (Hastie & Stuetzle, 1989) and manifolds (Gorban et al., 2008), have intuitive explanations and formulations comparing to the other non-linear dimensional techniques such as ISOMAP (Tenenbaum et al., 2000) and Locally-linear embedding (LLE) (Roweis & Saul, 2000).