Heuristic principal component analysis-based unsupervised feature extraction and its application to gene expression analysis of amyotrophic lateral sclerosis data sets.
Y-h. TaguchiMitsuo IwadateHideaki UmeyamaPublished in: CIBCB (2015)
Keyphrases
- principal component analysis
- gene expression analysis
- feature extraction
- data sets
- gene expression data
- microarray data
- dimensionality reduction
- microarray
- gene expression
- linear discriminant analysis
- face recognition
- amyotrophic lateral sclerosis
- feature space
- gene expression data analysis
- feature selection
- analysis of gene expression data
- independent component analysis
- high dimensional data
- low dimensional
- unsupervised learning
- pattern recognition
- gene clusters
- high dimensionality
- image processing
- feature set
- semi supervised
- supervised learning
- gene networks
- gene regulatory networks
- feature vectors
- gene selection
- training set
- medical images
- image analysis
- high throughput
- decision trees
- classification accuracy
- support vector machine svm
- computer vision
- machine learning