Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data.
Kosuke YoshidaJunichiro YoshimotoKenji DoyaPublished in: BMC Bioinform. (2017)
Keyphrases
- high dimensional data
- high dimension
- high dimensional
- kernel canonical correlation analysis
- sparse representation
- low dimensional
- dimensionality reduction
- underlying manifold
- nearest neighbor
- data analysis
- high dimensions
- subspace clustering
- original data
- data points
- manifold learning
- high dimensional spaces
- similarity search
- low dimensional manifolds
- dimension reduction
- dimensional data
- high dimensionality
- data sets
- input space
- input data
- high dimensional datasets
- clustering high dimensional data
- data mining
- linear discriminant analysis
- high dimensional feature space
- nonlinear dimensionality reduction
- high dimensional data sets
- subspace learning
- sparse coding
- random projections
- variable selection
- gene expression data
- knowledge discovery
- neural network
- high dimensional feature spaces
- lower dimensional
- locally linear embedding
- low rank
- multi dimensional
- similarity measure
- decision trees
- computer vision