Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings.
Yugo NakayamaKazuyoshi YataMakoto AoshimaPublished in: J. Multivar. Anal. (2021)
Keyphrases
- high dimension
- sample size
- small sample
- gaussian kernel
- covariance matrix
- principal component analysis
- support vector machine
- feature space
- model selection
- high dimensional data
- real valued
- kernel methods
- feature selection
- dimensionality reduction
- low dimensional
- high dimensional
- random sampling
- upper bound
- unsupervised learning
- input space
- reproducing kernel hilbert space
- support vector
- high dimensionality
- machine learning
- feature set
- face recognition
- ls svm
- feature extraction
- data points