Parametric characterization of multimodal distributions with non-Gaussian modes

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 286-292

  Tewari, Ashutosh ^a   Giering, Michael J ^a   Raghunathan, Arvind ^a,b  

^a UNITED TECHNOLOGIES RESEARCH CENTER   (United States)

^b MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

Author keywords

Copula function; Gaussian mixture models; Image segmentation; Mixture models; Non convex optimization

Indexed keywords

COPULA FUNCTIONS; DATA SETS; EFFICIENT LEARNING; EXPECTATION MAXIMIZATION; EXPERIMENTAL EVIDENCE; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURE MODELS; GAUSSIAN MIXTURES; GRADIENT-BASED OPTIMIZATION; LOG-LIKELIHOOD FUNCTIONS; MIXTURE MODEL; MIXTURE MODELS; MULTI-MODAL; MULTIMODAL DISTRIBUTIONS; NON-GAUSSIAN; NONCONVEX; NONCONVEX OPTIMIZATION; OPTIMAL SOLUTIONS; PARAMETRIC CHARACTERIZATION; PARAMETRIC MIXTURE MODEL; REAL LIFE DATASETS;

CONVEX OPTIMIZATION; DATA MINING; LEARNING ALGORITHMS; MIXING; NORMAL DISTRIBUTION;

IMAGE SEGMENTATION;

EID: 84857165130     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2011.135     Document Type: Conference Paper

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