Principal Component Analysis for Supervised Learning: a minimum classification error approach.
Tiago Buarque Assunção de CarvalhoMaria Aparecida Amorim SibaldoIng Ren TsangGeorge Darmiton da Cunha CavalcantiPublished in: J. Inf. Data Manag. (2017)
Keyphrases
- principal component analysis
- supervised learning
- minimum classification error
- statistical pattern recognition
- unsupervised learning
- dimensionality reduction
- principal components
- maximum likelihood estimation
- classification error
- covariance matrix
- hidden markov models
- discriminant analysis
- linear discriminant analysis
- discriminative training
- semi supervised learning
- training set
- gaussian mixture
- training data
- generalization error
- training samples
- semi supervised
- active learning
- labeled data
- reinforcement learning
- face recognition
- low dimensional
- face images
- feature extraction
- kernel pca
- learning algorithm
- class labels
- machine learning
- em algorithm
- feature space
- training examples
- probabilistic interpretation
- decision trees
- maximum likelihood