Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
研究了高维低样本量数据下基于核主成分分析的聚类方法，从理论上解释了高斯核的有效性，并讨论了尺度参数的选择，最后用微阵列数据验证了性能。
In this paper, we consider clustering based on the kernel principal component analysis (KPCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons why the Gaussian kernel is effective for clustering high-dimensional data. In addition, we discuss a choice of the scale parameter yielding a high performance of the KPCA with the Gaussian kernel. Finally, we test the performance of the clustering by using microarray data sets.