Spectral Clustering by Subspace Randomization and Graph Fusion for High-Dimensional Data.
Xiaosha CaiDong HuangChang-Dong WangChee-Keong KwohPublished in: PAKDD (1) (2020)
Keyphrases
- high dimensional data
- spectral clustering
- graph laplacian
- subspace clustering
- graph partitioning
- affinity matrix
- graph construction
- low dimensional
- normalized cut
- similarity matrix
- dimensionality reduction
- high dimensional
- clustering method
- nearest neighbor
- clustering high dimensional data
- data sets
- eigendecomposition
- pairwise
- dimension reduction
- high dimensionality
- data analysis
- data clustering
- similarity search
- data points
- clustering algorithm
- sparse representation
- subspace learning
- image segmentation
- linear discriminant analysis
- k means
- lower dimensional
- input space
- low rank
- manifold structure
- weighted graph
- clustering quality
- low rank representation
- input data
- linear subspace
- manifold learning
- kernel pca
- random walk
- machine learning
- data mining
- similarity measure
- graph matching
- euclidean distance