Detecting influential observations in Kernel PCA
نویسندگان
چکیده
Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Kernel PCA is characterized by calculating the influence function. A robust Kernel PCA method is proposed by incorporating kernels in the Spherical PCA algorithm. Using the scores from Spherical Kernel PCA, a graphical diagnostic is proposed to detect points that are influential for ordinary Kernel PCA.
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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 54 شماره
صفحات -
تاریخ انتشار 2010