Low-rank Feature Selection for Reinforcement Learning.
Bahram BehzadianMarek PetrikPublished in: ISAIM (2018)
Keyphrases
- low rank
- feature selection
- reinforcement learning
- linear combination
- missing data
- convex optimization
- kernel matrix
- matrix completion
- machine learning
- singular value decomposition
- matrix factorization
- semi supervised
- trace norm
- high dimensional data
- low rank matrix
- rank minimization
- multi task
- high order
- function approximation
- text categorization
- matrix decomposition
- learning algorithm
- dimensionality reduction
- mutual information
- state space
- kernel learning
- temporal difference
- low rank matrices
- text classification
- multi class
- minimization problems
- feature extraction
- image processing
- data matrix
- unsupervised feature selection
- robust principal component analysis
- transfer learning
- support vector machine
- knn
- learning process
- feature space
- support vector
- gene expression data
- supervised learning
- small number
- neural network
- data sets