A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering.
Yancheng YuanTsung-Hui ChangDefeng SunKim-Chuan TohPublished in: SIAM J. Optim. (2022)
Keyphrases
- dimension reduction
- optimization problems
- high dimensional
- high dimensional data
- cluster analysis
- principal component analysis
- high dimensional data analysis
- high dimensionality
- sparse metric learning
- evolutionary algorithm
- real world
- unsupervised learning
- feature space
- random projections
- singular value decomposition
- feature extraction
- high dimensional problems
- low dimensional
- manifold learning
- linear discriminant analysis
- clustering method
- sparse representation
- cost function
- clustering algorithm
- computer vision
- learning algorithm
- machine learning
- data clustering
- k means
- spectral clustering
- convex optimization
- variable selection
- data mining