Deterministic annealing for clustering, compression, classification, regression, and related optimization problems
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Summary
The deterministic annealing approach to clustering and its extensions has demonstrated substantial performance improvement over standard supervised and unsupervised learning methods in a variety of important applications including compression, estimation, pattern recognition and classification, and statistical regression.
- Type
- article
- Published
- 1998-11-01
- Cited by
- 1,026
- References
- 112
- OpenAlex
- https://openalex.org/W2161877964
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6175119
Keywords
Cluster analysis, Randomness, Simulated annealing, Computation, Computer science
References
- Deterministic annealing, clustering, and optimization
- Neural Networks and Related Methods for Classification
- Probability, Random Processes, And Ergodic Properties
- Principles and practice of information theory
- Vector quantization and signal compression
- Statistical pattern recognition with neural networks: benchmarking studies
- Vector quantizer design for memoryless noisy channels
- Nonconvex optimization by fast simulated annealing
- Stochastic Complexity and Modeling
- The design of joint source and channel trellis waveform coders
- A greedy tree growing algorithm for the design of variable rate vector quantizers [image compression]
- Information Theory and Reliable Communication
- A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
- A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Hierarchical, Unsupervised Learning with Growing via Phase Transitions
- Nonlinear gated experts for time series: discovering regimes and avoiding overfitting
- A clustering technique for summarizing multivariate data.
- A global optimization technique for statistical classifier design
- Statistical mechanics as the underlying theory of ‘elastic’ and ‘neural’ optimisations
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- Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning
- Creating hidden Markov models for fast speech by optimized clustering
- Statistical analysis of RNA-seq data from next- generation sequencing technology
- Counterexamples to convergence theorem of maximum-entropy clustering algorithm
- A New Information-Theoretic Measure to Control the Robustness-Sensitivity Trade-Off for DMFFD Point-Set Registration
- Extraction of Protein Domains and Signatures through Unsupervised Statistical Sequence Segmentation
- Advanced Techniques for Scene Analysis
- A Unified Approach to Minimum Risk Training and Decoding
- Application of K-tree to document clustering
- Discriminative training of tied-mixture HMM by deterministic annealing
- Clustering with Model-level Constraints
- Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework
- Unified Expectation Maximization
- FUZZY CLUSTERING ALGORITHMS ON LANDSAT IMAGES FOR DETECTION OF WASTE AREAS: A COMPARISON
- Search for lepton-flavour-violating decays of the Higgs boson
- Correspondences between Salient Points on 3D Shapes
- Unsupervised Clustering of Images using their Joint Segmentation
- Document clustering algorithms, representations and evaluation for information retrieval
- Topologically Ordered Graph Clustering via Deterministic Annealing
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