Dimensionality Reduction for k-Means Clustering and Low Rank Approximation.
Michael B. CohenSam ElderCameron MuscoChristopher MuscoMadalina PersuPublished in: CoRR (2014)
Keyphrases
- low rank approximation
- dimensionality reduction
- subspace learning
- singular value decomposition
- spectral clustering
- low rank matrix approximation
- high dimensional data
- principal component analysis
- pattern recognition
- low dimensional
- feature space
- high dimensional
- data representation
- high dimensionality
- low rank
- manifold learning
- unsupervised learning
- feature extraction
- k means
- feature selection
- dimension reduction
- data points
- kernel matrix
- sparse representation
- principal components
- metric learning
- input space
- reconstruction error
- pairwise
- latent semantic indexing
- cluster analysis
- machine learning
- iterative algorithms
- eigendecomposition