FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2080104526> ?p ?o ?g. }

• W2080104526 endingPage "1862" @default.
• W2080104526 startingPage "1850" @default.
• W2080104526 abstract "A large class of problems can be formulated in terms of the clustering process. Mixture models are an increasingly important tool in statistical pattern recognition and for analyzing and clustering complex data. Two challenging aspects that should be addressed when considering mixture models are how to choose between a set of plausible models and how to estimate the model's parameters. In this paper, we address both problems simultaneously within a unified online nonparametric Bayesian framework that we develop to learn a Dirichlet process mixture of Beta-Liouville distributions (i.e., an infinite Beta-Liouville mixture model). The proposed infinite model is used for the online modeling and clustering of proportional data for which the Beta-Liouville mixture has been shown to be effective. We propose a principled approach for approximating the intractable model's posterior distribution by a tractable one-which we develop-such that all the involved mixture's parameters can be estimated simultaneously and effectively in a closed form. This is done through variational inference that enjoys important advantages, such as handling of unobserved attributes and preventing under or overfitting; we explain that in detail. The effectiveness of the proposed work is evaluated on three challenging real applications, namely facial expression recognition, behavior modeling and recognition, and dynamic textures clustering." @default.
• W2080104526 created "2016-06-24" @default.
• W2080104526 creator A5056455910 @default.
• W2080104526 creator A5090600716 @default.
• W2080104526 date "2013-11-01" @default.
• W2080104526 modified "2023-09-27" @default.
• W2080104526 title "Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference" @default.
• W2080104526 cites W1509031088 @default.
• W2080104526 cites W1517555081 @default.
• W2080104526 cites W1520448186 @default.
• W2080104526 cites W1696883162 @default.
• W2080104526 cites W1970789124 @default.
• W2080104526 cites W1972525513 @default.
• W2080104526 cites W1976566382 @default.
• W2080104526 cites W1981796042 @default.
• W2080104526 cites W1982116695 @default.
• W2080104526 cites W1989198448 @default.
• W2080104526 cites W1989692914 @default.
• W2080104526 cites W1992419399 @default.
• W2080104526 cites W1992960277 @default.
• W2080104526 cites W1995444699 @default.
• W2080104526 cites W2000930721 @default.
• W2080104526 cites W2001983900 @default.
• W2080104526 cites W2005913992 @default.
• W2080104526 cites W2030085689 @default.
• W2080104526 cites W2039076379 @default.
• W2080104526 cites W2050453099 @default.
• W2080104526 cites W2067001895 @default.
• W2080104526 cites W2067776296 @default.
• W2080104526 cites W2070047497 @default.
• W2080104526 cites W2077786999 @default.
• W2080104526 cites W2080972498 @default.
• W2080104526 cites W2091328698 @default.
• W2080104526 cites W2096784803 @default.
• W2080104526 cites W2101393962 @default.
• W2080104526 cites W2106390385 @default.
• W2080104526 cites W2120211304 @default.
• W2080104526 cites W2124716447 @default.
• W2080104526 cites W2125956916 @default.
• W2080104526 cites W2127498532 @default.
• W2080104526 cites W2129977294 @default.
• W2080104526 cites W2133180260 @default.
• W2080104526 cites W2134529554 @default.
• W2080104526 cites W2134577338 @default.
• W2080104526 cites W2134731454 @default.
• W2080104526 cites W2139916508 @default.
• W2080104526 cites W2144245426 @default.
• W2080104526 cites W2145047463 @default.
• W2080104526 cites W2145310492 @default.
• W2080104526 cites W2151709124 @default.
• W2080104526 cites W2157487910 @default.
• W2080104526 cites W2158266063 @default.
• W2080104526 cites W2159017231 @default.
• W2080104526 cites W2160947254 @default.
• W2080104526 cites W2163808566 @default.
• W2080104526 cites W2165072487 @default.
• W2080104526 cites W2165183780 @default.
• W2080104526 cites W2168341643 @default.
• W2080104526 cites W2170168257 @default.
• W2080104526 cites W2170902875 @default.
• W2080104526 cites W2171911691 @default.
• W2080104526 cites W2296770417 @default.
• W2080104526 cites W2488678869 @default.
• W2080104526 cites W2533739470 @default.
• W2080104526 cites W2912155302 @default.
• W2080104526 cites W2983597079 @default.
• W2080104526 cites W4212863985 @default.
• W2080104526 cites W4237840503 @default.
• W2080104526 cites W4292403327 @default.
• W2080104526 cites W4300246981 @default.
• W2080104526 doi "https://doi.org/10.1109/tnnls.2013.2268461" @default.
• W2080104526 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24808617" @default.
• W2080104526 hasPublicationYear "2013" @default.
• W2080104526 type Work @default.
• W2080104526 sameAs 2080104526 @default.
• W2080104526 citedByCount "46" @default.
• W2080104526 countsByYear W20801045262012 @default.
• W2080104526 countsByYear W20801045262013 @default.
• W2080104526 countsByYear W20801045262016 @default.
• W2080104526 countsByYear W20801045262017 @default.
• W2080104526 countsByYear W20801045262018 @default.
• W2080104526 countsByYear W20801045262019 @default.
• W2080104526 countsByYear W20801045262020 @default.
• W2080104526 countsByYear W20801045262021 @default.
• W2080104526 countsByYear W20801045262022 @default.
• W2080104526 crossrefType "journal-article" @default.
• W2080104526 hasAuthorship W2080104526A5056455910 @default.
• W2080104526 hasAuthorship W2080104526A5090600716 @default.
• W2080104526 hasConcept C107673813 @default.
• W2080104526 hasConcept C119857082 @default.
• W2080104526 hasConcept C134306372 @default.
• W2080104526 hasConcept C153180895 @default.
• W2080104526 hasConcept C154945302 @default.
• W2080104526 hasConcept C160234255 @default.
• W2080104526 hasConcept C169214877 @default.
• W2080104526 hasConcept C182310444 @default.
• W2080104526 hasConcept C22019652 @default.
• W2080104526 hasConcept C2776214188 @default.

• next