A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data.
Chun-Qiu XiaKe HanYong QiYang ZhangDong-Jun YuPublished in: IEEE ACM Trans. Comput. Biol. Bioinform. (2018)
Keyphrases
- low rank representation
- cancer classification
- gene expression data
- clustering algorithm
- low rank
- microarray
- gene selection
- gene expression
- gene expression profiling
- high dimensional data
- cancer diagnosis
- microarray data
- spectral clustering
- microarray gene expression data
- gene expression datasets
- gene expression profiles
- clustering analysis
- semi supervised
- feature selection
- colon cancer
- gene expression analysis
- gene regulatory networks
- high dimensionality
- linear subspace
- semi supervised learning
- subspace clustering
- dna microarray
- data sets
- high dimensional
- cluster analysis
- data clustering
- missing data
- clustering method
- cancer datasets
- k means
- support vector
- random forest
- dimensionality reduction
- tissue samples
- informative genes
- neural network
- data mining
- low dimensional
- nearest neighbor
- microarray datasets
- experimental conditions
- normalized cut