Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-Clustering to Find "Checkerboard" Structures on Gene Expression Data.
Jin-Xing LiuChun-Mei FengXiang-Zhen KongYong XuPublished in: IEEE Access (2019)
Keyphrases
- gene expression data
- graph laplacian
- spectral clustering
- high dimensionality
- gene expression analysis
- gene expression
- gene expression profiles
- microarray
- k means
- high dimensional
- high dimensional data
- principal component analysis
- dimensionality reduction
- clustering method
- data sets
- clustering algorithm
- random walk
- data clustering
- biclustering algorithms
- feature selection
- feature space
- spectral analysis
- data points
- manifold structure
- low dimensional
- unsupervised learning
- euclidean space
- cluster analysis
- basis functions
- pairwise
- active learning
- negative matrix factorization
- nearest neighbor
- kernel machines
- semi supervised learning
- computer vision