Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
复制标题
在高维、低样本量设置中通过主成分分析与高斯核进行聚类
DOI：
10.1016/j.jmva.2021.104779
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发表时间：
2021
影响因子：
1.6
通讯作者：
Aoshima Makoto
中科院分区：
文献类型：
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作者：
Nakayama Yugo;Yata Kazuyoshi;Aoshima Makoto
针对高维、低样本(HDLSS)数据，提出了基于核主成分分析(KPCA)的聚类方法。我们给出了为什么高斯核在高维数据聚类中是有效的理论原因。此外，我们还讨论了尺度参数的选择，以获得高性能的高斯核KPCA。最后，我们使用微阵列数据集来测试该聚类的性能。
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.