Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE).
Farzana AnowarSamira SadaouiBassant SelimPublished in: Comput. Sci. Rev. (2021)
Keyphrases
- dimensionality reduction
- principal component analysis
- linear discriminant analysis
- dimensionality reduction methods
- locally linear embedding
- low dimensional
- principal components analysis
- independent component analysis
- feature extraction
- singular value decomposition
- high dimensional data
- multidimensional scaling
- feature space
- dimension reduction
- face recognition
- nonlinear dimensionality reduction
- high dimensional
- principal components
- kernel pca
- linear dimensionality reduction
- discriminant analysis
- pca lda
- high dimensionality
- manifold learning
- lower dimensional
- input space
- subspace methods
- manifold learning algorithm
- subspace learning
- pattern recognition
- kernel principal component analysis
- feature selection
- random projections
- graph embedding
- neighborhood preserving
- euclidean distance
- dimension reduction methods
- supervised dimensionality reduction
- face images
- data points
- latent dirichlet allocation
- metric learning
- sparse representation
- principle component analysis
- data sets
- machine learning
- image processing
- multi dimensional scaling
- feature mapping
- preprocessing
- locality preserving projections
- null space
- high dimensional feature space
- covariance matrix