Dimensionality Reduction for k-Means Clustering and Low Rank Approximation.
Michael B. CohenSam ElderCameron MuscoChristopher MuscoMadalina PersuPublished in: STOC (2015)
Keyphrases
- low rank approximation
- dimensionality reduction
- subspace learning
- singular value decomposition
- spectral clustering
- low rank matrix approximation
- principal component analysis
- high dimensional
- high dimensional data
- low dimensional
- low rank
- pattern recognition
- manifold learning
- data points
- feature extraction
- k means
- high dimensionality
- principal components
- feature selection
- unsupervised learning
- dimension reduction
- input space
- data representation
- latent semantic indexing
- clustering algorithm
- euclidean distance
- feature space
- data clustering
- least squares
- kernel matrix
- nonnegative matrix factorization
- data sets
- metric learning
- feature vectors