講題: Robust kernel principal component analysis
演講者：陳素雲 研究員(中央研究院統計科學研究所)
Abstract :We will first introduce the kernel principal component analysis as a nonlinear
extension for classical PCA. We will then discuss the robustness issue of kernel
PCA. A class of new robust procedures is proposed based on
eigenvalue decomposition of weighted kernel covariance. The proposed
procedures will place less weights to deviant patterns and thus
behave more resistant to data contamination and model deviation.
Theoretical influence functions are derived and numerical examples
are presented as well. Both theoretical and numerical results
indicate that the proposed robust method outperforms the
conventional approach in the sense of being less sensitive to
outliers. Our robust method and results also apply to functional
principal component analysis.
時間：2010年10月26日(星期二) 14：00 ∼ 17：00
地點：推廣大樓3樓9313室
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