-norm based structural sparse least square regression for feature selection.
Jiuqi HanZhengya SunHongwei HaoPublished in: Pattern Recognit. (2015)
Keyphrases
- feature selection
- sparse pca
- norm regularization
- low rank matrices
- web image annotation
- mutual information
- group lasso
- structural information
- high dimensionality
- variable selection
- robust principal component analysis
- feature selection algorithms
- sparse learning
- text categorization
- high dimension
- information gain
- machine learning
- high dimensional
- feature extraction
- classification accuracy
- text classification
- feature space
- sparse representation
- mixed norm
- regularized least squares
- irrelevant features
- multi task
- feature set
- sparse data
- compressive sensing
- selected features
- objective function
- sparsity inducing
- convex optimization
- feature subset
- support vector
- small sample
- structured sparsity
- data sets
- multi class
- canonical correlation analysis
- discriminative features
- dimensionality reduction
- low rank
- sparse coding
- model selection
- image restoration