DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling.
Rundong DuDa KuangBarry L. DrakeHaesun ParkPublished in: J. Glob. Optim. (2017)
Keyphrases
- nonnegative matrix factorization
- topic modeling
- negative matrix factorization
- topic models
- probabilistic latent semantic indexing
- nonnegative matrix
- matrix factorization
- spectral clustering
- collaborative filtering
- data representation
- latent dirichlet allocation
- least squares
- latent structure
- document clustering
- text mining
- probabilistic latent semantic analysis
- principal component analysis
- original data
- data matrix
- clustering algorithm
- text documents
- clustering method
- dimensionality reduction
- k means
- recommender systems
- matrix factorisation
- data sets
- latent variables
- data clustering
- missing data
- text classification
- pattern recognition
- objective function
- information retrieval
- databases