Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations.
Kazuyoshi YataMakoto AoshimaPublished in: J. Multivar. Anal. (2012)
Keyphrases
- sample size
- noise reduction
- high dimension
- data sets
- small sample
- input space
- data structure
- high dimensional data
- input data
- principal component analysis
- random sampling
- covariance matrix
- image data
- data points
- high dimensional
- training data
- small samples
- statistical power
- training examples
- random sample
- pattern recognition
- objective function
- feature extraction
- machine learning