Representing Input Transformations by Low-Dimensional Parameter Subspaces.
Olga SaukhDong WangXiaoxi HeLothar ThielePublished in: CoRR (2023)
Keyphrases
- low dimensional
- high dimensional
- high dimensional data
- principal component analysis
- data points
- dimensionality reduction
- input parameters
- lower dimensional
- linear subspace
- manifold learning
- euclidean space
- input data
- multidimensional scaling
- parameter space
- dimension reduction
- input space
- nonlinear dimensionality reduction
- subspace learning
- feature space
- parameter values
- vector space
- subspace analysis
- linear dimensionality reduction
- subspace clustering
- parameter settings
- user input
- original data
- sparse representation
- latent space
- data analysis
- high dimensional data space