SLISEMAP: Explainable Dimensionality Reduction.
Anton BjörklundJarmo MäkeläKai PuolamäkiPublished in: CoRR (2022)
Keyphrases
- dimensionality reduction
- high dimensional
- high dimensional data
- low dimensional
- pattern recognition
- high dimensionality
- principal component analysis
- manifold learning
- feature space
- linear discriminant analysis
- feature selection
- data representation
- random projections
- nonlinear dimensionality reduction
- feature extraction
- data points
- structure preserving
- pattern recognition and machine learning
- input space
- euclidean distance
- metric learning
- principal components
- dimensionality reduction methods
- linear dimensionality reduction
- dimension reduction
- lower dimensional
- kernel learning
- supervised dimensionality reduction
- locally linear embedding
- semi supervised dimensionality reduction
- support vector machine
- unsupervised feature selection
- singular value decomposition
- multidimensional scaling
- kernel pca
- preprocessing step