A Probabilistic PCA Clustering Approach to the SVD Estimate of Signal Subspaces.
Massimo PanellaG. GrisantiAntonello RizziPublished in: WIRN (2004)
Keyphrases
- principal component analysis
- singular value decomposition
- dimensionality reduction
- high dimensional data
- k means
- low dimensional
- principal components analysis
- dimension reduction
- data points
- high dimensional
- feature space
- feature subspace
- high dimensionality
- independent component analysis
- signal processing
- clustering algorithm
- clustering method
- principal components
- lower dimensional
- independent components
- information theoretic
- feature extraction
- probabilistic model
- hierarchical clustering
- least squares
- tensor analysis
- bayesian networks
- linear subspace
- non stationary
- self organizing maps
- cluster analysis
- spectral clustering
- covariance matrix
- document clustering
- singular values
- generative model
- random projections
- nearest neighbor
- nonnegative matrix factorization
- feature selection