Hierarchical GTM: Constructing localized nonlinear projection manifolds in a principled way

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 24, Issue 5, 2002, Pages 639-656

  Tino, Peter ^a   Nabney, Ian ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

Data visualization; Density estimation; Directional curvature; EM algorithm; Generative topographic mapping; Hierarchical probabilistic model

Indexed keywords

ALGORITHMS; GEOMETRY; HIERARCHICAL SYSTEMS; IMAGE ANALYSIS; IMAGE SEGMENTATION; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; PROBABILITY DENSITY FUNCTION; SELF ORGANIZING MAPS; TREES (MATHEMATICS); VISUALIZATION;

DATA VISUALIZATION; DENSITY ESTIMATION; DIRECTIONAL CURVATURE; EXPECTATION-MAXIMIZATION ALGORITHM; GENERATIVE TOPOGRAPHIC MAPPING; HIERARCHICAL PROBABILISTIC MODEL; HIERARCHICAL VISUALIZATION SYSTEM; MAGNIFICATION FACTORS; PROJECTION MANIFOLDS;

PATTERN RECOGNITION SYSTEMS;

EID: 0036565797     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.1000238     Document Type: Article

References (23)

1. A hierarchical latent variable model for data visualization
2. GTM: The generative topographic mapping
3. The self-organizing map
4. Developments of the generative topographic mapping
5. Magnification factors for the SOM and GTM algorithms
6. Script recognition with hierarchical feature maps
7. Learning fine motion by using the hierarchical extended Kohenen map
8. Learning fine motion in robotics: Experiments with the hierarchical extended Kohonen map
9. A MCMC approach to hierarchical mixture modelling
10. Maximum likelihood from incomplete data via the EM algorithm
11. Voronoi diagrams-survey of a fundamental geometric data structure
12. Mixtures of probabilistic principal component analyzers
13. Magnification factors for the GTM algorithm
14. Kohonen's self organizing feature maps for exploratory data analysis
15. Knowledge extraction from self-organizing neural networks
16. SOM-based data visualization methods
17. Relative curvature measures of nonlinearity (with discussion)
18. Statistical mechanics and phase transitions in clustering