Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
Abstract
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.
Topics & Concepts
Kernel principal component analysisMathematicsCluster analysisPrincipal component analysisKernel (algebra)Clustering high-dimensional dataPattern recognition (psychology)Dimension (graph theory)Variable kernel density estimationStatisticsGaussianKernel methodArtificial intelligenceComputer scienceCombinatoricsSupport vector machinePhysicsQuantum mechanicsGene expression and cancer classificationFace and Expression RecognitionBayesian Methods and Mixture Models