Reduced {k)-means clustering with MCA in a low-dimensional space.
Masaki MitsuhiroHiroshi YadohisaPublished in: Comput. Stat. (2015)
Keyphrases
- low dimensional
- high dimensional
- dimensionality reduction
- lower dimensional
- high dimensional data
- principal component analysis
- higher dimensional
- data points
- input space
- manifold learning
- euclidean space
- vector space
- linear subspace
- dimension reduction
- low dimensional manifolds
- high dimensional spaces
- feature space
- search space
- subspace learning
- face recognition
- neural network
- parameter space
- space time
- k means
- support vector
- nonlinear dimensionality reduction
- graph embedding