A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection.
Chun-Mei FengJin-Xing LiuYing-Lian GaoJuan WangDong-Qin WangYong DuPublished in: BIBM (2016)
Keyphrases
- gene selection
- graph laplacian
- microarray data
- random walk
- microarray
- spectral clustering
- euclidean space
- spectral analysis
- gene expression data
- basis functions
- gene expression
- weighted graph
- kernel machines
- pointwise
- semi supervised learning
- support vector machine svm
- neighborhood graph
- principal component analysis
- machine learning
- labeled and unlabeled data
- feature selection
- support vector
- computer vision
- probabilistic model
- support vector machine
- high dimensionality
- data sets