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: CoRR (2019)
Keyphrases
- gene expression data
- graph laplacian
- spectral clustering
- gene expression analysis
- high dimensionality
- gene expression
- microarray
- high dimensional data
- k means
- gene expression profiles
- high dimensional
- dimensionality reduction
- principal component analysis
- clustering algorithm
- data sets
- clustering method
- data clustering
- random walk
- low dimensional
- kernel machines
- biclustering algorithms
- data points
- manifold structure
- feature space
- spectral analysis
- euclidean space
- negative matrix factorization
- neural network
- basis functions
- machine learning
- face recognition
- unsupervised learning
- feature selection
- feature extraction
- dimension reduction
- pairwise
- cluster analysis
- distance metric
- semi supervised learning
- manifold learning
- image segmentation