Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix.
Kazuyoshi YataMakoto AoshimaPublished in: J. Multivar. Anal. (2010)
Keyphrases
- singular value decomposition
- sample size
- data matrix
- high dimension
- original data
- high dimensional data
- principal component analysis
- dimensionality reduction
- data sets
- low rank approximation
- training data
- data points
- high dimensional
- missing values
- dimension reduction
- input space
- feature extraction
- pattern recognition
- singular values
- small sample
- raw data
- semi supervised
- model selection
- data analysis
- matrix factorization
- low rank
- missing data
- low dimensional