FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226320676> ?p ?o ?g. }

• W4226320676 endingPage "102" @default.
• W4226320676 startingPage "89" @default.
• W4226320676 abstract "Clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation process is slow. The exponential-family approximation of the multivariate Polya distribution has shown to be efficient to train and cluster data directly, without dimensionality reduction. In this article, we derive an exponential-family approximation to the GDM distributions, and we call it (EGDM). A mixture model is developed based on the new member of the exponential-family of distributions, and its parameters are learned through the deterministic annealing expectation-maximization (DAEM) approach as a new clustering algorithm for count data. Moreover, we propose to estimate the optimal number of EGDM mixture components based on the minimum message length (MML) criterion. We have conducted a set of empirical experiments, concerning text, image, and video clustering, to evaluate the proposed approach performance. Results show that the new model attains a superior performance, and it is considerably faster than the corresponding method for GDM distributions." @default.
• W4226320676 created "2022-05-05" @default.
• W4226320676 creator A5060719711 @default.
• W4226320676 creator A5090600716 @default.
• W4226320676 date "2022-01-01" @default.
• W4226320676 modified "2023-09-26" @default.
• W4226320676 title "Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions" @default.
• W4226320676 cites W1539587635 @default.
• W4226320676 cites W1566135517 @default.
• W4226320676 cites W1575363794 @default.
• W4226320676 cites W1594924988 @default.
• W4226320676 cites W1603978410 @default.
• W4226320676 cites W1790954942 @default.
• W4226320676 cites W1986333823 @default.
• W4226320676 cites W1990442826 @default.
• W4226320676 cites W2007463795 @default.
• W4226320676 cites W2009156926 @default.
• W4226320676 cites W2011832962 @default.
• W4226320676 cites W2015245929 @default.
• W4226320676 cites W2017814585 @default.
• W4226320676 cites W2025653016 @default.
• W4226320676 cites W2032079298 @default.
• W4226320676 cites W2033160484 @default.
• W4226320676 cites W2048695473 @default.
• W4226320676 cites W2054658115 @default.
• W4226320676 cites W2055325763 @default.
• W4226320676 cites W2055337076 @default.
• W4226320676 cites W2060003092 @default.
• W4226320676 cites W2068143350 @default.
• W4226320676 cites W2077041217 @default.
• W4226320676 cites W2090050657 @default.
• W4226320676 cites W2096784803 @default.
• W4226320676 cites W2100736366 @default.
• W4226320676 cites W2100916003 @default.
• W4226320676 cites W2101194540 @default.
• W4226320676 cites W2110381504 @default.
• W4226320676 cites W2113110240 @default.
• W4226320676 cites W2116502739 @default.
• W4226320676 cites W2128299335 @default.
• W4226320676 cites W2130843763 @default.
• W4226320676 cites W2131846894 @default.
• W4226320676 cites W2135631383 @default.
• W4226320676 cites W2140942285 @default.
• W4226320676 cites W2141362318 @default.
• W4226320676 cites W2142635246 @default.
• W4226320676 cites W2143869282 @default.
• W4226320676 cites W2151103935 @default.
• W4226320676 cites W2154468968 @default.
• W4226320676 cites W2279382301 @default.
• W4226320676 cites W229441436 @default.
• W4226320676 cites W2807567715 @default.
• W4226320676 cites W2901065680 @default.
• W4226320676 cites W2921645317 @default.
• W4226320676 cites W2968303400 @default.
• W4226320676 cites W4213009331 @default.
• W4226320676 cites W4229633200 @default.
• W4226320676 cites W4238346259 @default.
• W4226320676 cites W4293052541 @default.
• W4226320676 doi "https://doi.org/10.1109/tnnls.2020.3027539" @default.
• W4226320676 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33079676" @default.
• W4226320676 hasPublicationYear "2022" @default.
• W4226320676 type Work @default.
• W4226320676 citedByCount "3" @default.
• W4226320676 countsByYear W42263206762019 @default.
• W4226320676 countsByYear W42263206762022 @default.
• W4226320676 countsByYear W42263206762023 @default.
• W4226320676 crossrefType "journal-article" @default.
• W4226320676 hasAuthorship W4226320676A5060719711 @default.
• W4226320676 hasAuthorship W4226320676A5090600716 @default.
• W4226320676 hasConcept C100906024 @default.
• W4226320676 hasConcept C105795698 @default.
• W4226320676 hasConcept C107673813 @default.
• W4226320676 hasConcept C11413529 @default.
• W4226320676 hasConcept C126255220 @default.
• W4226320676 hasConcept C134306372 @default.
• W4226320676 hasConcept C153180895 @default.
• W4226320676 hasConcept C154945302 @default.
• W4226320676 hasConcept C169214877 @default.
• W4226320676 hasConcept C182081679 @default.
• W4226320676 hasConcept C182310444 @default.
• W4226320676 hasConcept C183057437 @default.
• W4226320676 hasConcept C192065140 @default.
• W4226320676 hasConcept C2781280628 @default.
• W4226320676 hasConcept C28826006 @default.
• W4226320676 hasConcept C33643355 @default.
• W4226320676 hasConcept C33923547 @default.
• W4226320676 hasConcept C41008148 @default.
• W4226320676 hasConcept C49781872 @default.
• W4226320676 hasConcept C55350006 @default.
• W4226320676 hasConcept C55974624 @default.
• W4226320676 hasConcept C61224824 @default.
• W4226320676 hasConcept C70518039 @default.
• W4226320676 hasConcept C73555534 @default.
• W4226320676 hasConceptScore W4226320676C100906024 @default.
• W4226320676 hasConceptScore W4226320676C105795698 @default.
• W4226320676 hasConceptScore W4226320676C107673813 @default.
• W4226320676 hasConceptScore W4226320676C11413529 @default.
• W4226320676 hasConceptScore W4226320676C126255220 @default.

• next