On asymptotic normality of cross data matrix-based PCA in high dimension low sample size.
Shao-Hsuan WangSu-Yun HuangTing-Li ChenPublished in: J. Multivar. Anal. (2020)
Keyphrases
- high dimension
- sample size
- small sample
- data matrix
- covariance matrix
- singular value decomposition
- principal component analysis
- principal components
- high dimensional
- feature space
- nonnegative matrix factorization
- worst case
- original data
- model selection
- missing values
- matrix factorization
- dimensionality reduction
- gene expression data
- random sampling
- barcode
- low rank
- real valued
- input space
- high dimensional data
- upper bound
- feature selection
- negative matrix factorization
- support vector machine
- singular values
- low dimensional
- hyperparameters
- random projections
- machine learning
- pattern recognition
- data sets
- objective function
- feature extraction
- classification accuracy
- knn