Dimensionality Reduction for Graph of Words Embedding.
Jaume GibertErnest ValvenyHorst BunkePublished in: GbRPR (2011)
Keyphrases
- dimensionality reduction
- graph embedding
- nonlinear dimensionality reduction
- structure preserving
- low dimensional
- embedding space
- graph representation
- multidimensional scaling
- principal component analysis
- data representation
- random walk
- feature extraction
- data points
- high dimensional data
- graph theory
- graph model
- subspace learning
- n gram
- high dimensional
- spectral embedding
- dimensionality reduction methods
- structured data
- feature space
- high dimensionality
- low dimensional spaces
- locality preserving projections
- connected components
- graph structure
- word sense disambiguation
- graph matching
- principal components
- linear discriminant analysis
- directed graph
- text documents
- semi supervised
- pattern recognition
- data sets
- nodes of a graph
- kernel pca
- directed acyclic graph
- weighted graph
- input space
- manifold learning
- euclidean distance
- keywords
- computer vision
- machine learning