Latent regularization for feature selection using kernel methods in tumor classification.
Martin PalazzoPatricio YankilevichPierre BeauseroyPublished in: CoRR (2020)
Keyphrases
- kernel methods
- tumor classification
- feature selection
- reproducing kernel hilbert space
- kernel ridge regression
- support vector
- feature space
- support vector machine
- kernel matrices
- machine learning
- gene selection
- kernel function
- gene expression data
- reproducing kernel
- kernel matrix
- classification accuracy
- text classification
- learning problems
- multi class
- multi step
- feature selection algorithms
- learning tasks
- feature set
- microarray data
- high dimensionality
- knn
- kernel learning
- dimensionality reduction
- feature extraction
- k nearest neighbor
- preprocessing step
- model selection
- selected features
- multiple kernel learning
- gene expression profiles
- neural network
- microarray
- image processing
- training data
- feature ranking
- feature vectors
- input data
- support vector machine svm
- discriminant analysis
- principal component analysis