Discriminative Clustering via Generative Feature Mapping
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Summary
This paper takes the advantages of both models of generative and discriminative clustering by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers, and proposes the unified probabilistic framework.
- Type
- article
- Published
- 2012-07-22
- Cited by
- 4
- References
- 29
- Access
- Open access
- OpenAlex
- https://openalex.org/W63921175
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17003108
Keywords
Discriminative model, Cluster analysis, Generative grammar, Computer science, Artificial intelligence
References
- Tighter and Convex Maximum Margin Clustering
- Unsupervised and Semi-Supervised Multi-Class Support Vector Machines
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Pattern Recognition and Machine Learning
- An Introduction to Variational Methods for Graphical Models
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Hybrid generative-discriminative classification using posterior divergence
- The Elements of Statistical Learning
- Discriminative Clustering by Regularized Information Maximization
- Free energy score space
- DIFFRAC: a discriminative and flexible framework for clustering
- Gradient-based learning applied to document recognition
- Expectation Maximization and Posterior Constraints
- Parallel Spectral Clustering in Distributed Systems
- Maximum Margin Clustering Made Practical
- Maximum Margin Clustering
- Normalized cuts and image segmentation
- I-Divergence Geometry of Probability Distributions and Minimization Problems
- Learning Generative Models via Discriminative Approaches
- On Spectral Clustering: Analysis and an algorithm
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