2D-EM clustering approach for high-dimensional data through folding feature vectors.
Alok SharmaPiotr J. KamolaTatsuhiko TsunodaPublished in: BMC Bioinform. (2017)
Keyphrases
- high dimensional data
- feature vectors
- dimensionality reduction
- data points
- high dimensionality
- subspace clustering
- high dimensional
- nearest neighbor
- high dimensions
- data sets
- low dimensional
- high dimensional data sets
- feature space
- k means
- input data
- original data
- similarity search
- gaussian mixture model
- data analysis
- dimension reduction
- clustering high dimensional data
- euclidean distance
- manifold learning
- unsupervised learning
- feature extraction
- nonlinear dimensionality reduction
- dimensional data
- high dimensional datasets
- face images
- lower dimensional
- high dimensional spaces
- small sample size
- input space
- text data
- expectation maximization
- support vector machine
- em algorithm
- similarity measure
- data clustering