Large Basic Cone and Sparse Subspace Constrained Nonnegative Matrix Factorization With Kullback-Leibler Divergence for Data Representation.
Viet-Hang DuongManh-Quan BuiYung-Hui LiJia-Ching WangTzu-Chiang TaiPublished in: IEEE Intell. Syst. (2019)
Keyphrases
- data representation
- nonnegative matrix factorization
- kullback leibler divergence
- subspace learning
- dimensionality reduction
- high dimensional
- mutual information
- information theoretic
- probability density function
- sparse representation
- distance measure
- low dimensional
- high dimensional data
- feature space
- principal component analysis
- sparse coding
- negative matrix factorization
- unsupervised learning
- pattern recognition
- feature extraction
- face recognition
- xml schema
- least squares
- metric learning
- xml documents
- data analysis