A convex multi-view low-rank sparse regression for feature selection and clustering.
Yao LuJin-Xing LiuXiangzhen KongJunliang ShangPublished in: BIBM (2017)
Keyphrases
- multi view
- sparse regression
- low rank
- convex optimization
- semi supervised
- feature selection and classification
- feature selection
- high dimensional data
- unsupervised learning
- high dimensionality
- unsupervised feature selection
- missing data
- data matrix
- matrix factorization
- linear combination
- three dimensional
- matrix completion
- d objects
- semi supervised learning
- k means
- pairwise constraints
- singular value decomposition
- convex relaxation
- dimensionality reduction
- pairwise
- high order
- nonnegative matrix factorization
- machine learning
- text classification
- data points
- feature extraction
- feature vectors
- active learning
- nearest neighbor
- labeled data
- supervised learning
- data analysis
- document clustering
- feature space