Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection.
Sara SharifzadehAli GhodsiLine H. ClemmensenBjarne K. ErsbøllPublished in: Eng. Appl. Artif. Intell. (2017)
Keyphrases
- dimension reduction
- variable selection
- principal component analysis
- group lasso
- random projections
- high dimensional
- sparse metric learning
- input variables
- low dimensional
- dimensionality reduction
- unsupervised learning
- sparsity inducing
- linear discriminant analysis
- feature selection
- principal components
- dimension reduction methods
- high dimensional data
- manifold learning
- feature extraction
- face recognition
- structured sparsity
- fisher discriminant analysis
- feature space
- sparse representation
- ls svm
- sparse coding
- high dimensional data analysis
- supervised learning
- semi supervised
- learning algorithm
- supervised dimensionality reduction
- lower dimensional
- singular value decomposition
- discriminant analysis
- cluster analysis
- data mining
- support vector machine
- regression model
- least squares
- active learning
- preprocessing
- machine learning