Inductive vs. transductive clustering using kernel functions and pairwise constraints.
Sadaaki MiyamotoA. TeramiPublished in: ISDA (2011)
Keyphrases
- pairwise constraints
- kernel function
- kernel learning
- semi supervised
- semi supervised clustering
- support vector
- decision boundary
- unlabeled data
- labeled data
- spectral clustering
- pairwise
- kernel matrix
- semi supervised learning
- document clustering
- kernel methods
- loss function
- feature space
- input space
- data representation
- distance metric
- machine learning
- svm classifier
- unsupervised learning
- active learning
- hyperplane
- support vector machine
- data points
- data clustering
- clustering method
- k means
- metric learning
- supervised learning
- clustering algorithm
- low rank
- support vectors
- similarity measure
- multiple kernel learning
- reproducing kernel hilbert space
- high dimensional
- face recognition
- feature set
- similarity matrix
- image processing
- data sets