LAUSR

Experiments with learning graphical models on text

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1 Published in 2018 at "Behaviormetrika"

DOI: 10.1007/s41237-018-0050-3

Abstract: A rich variety of models are now in use for unsupervised modelling of text documents, and, in particular, a rich variety of graphical models exist, with and without latent variables. To date, there is inadequate… read more here.

Keywords: models text; experiments learning; matrix factorisation; learning graphical ... See more keywords

Using non-negative matrix factorisation to facilitate efficient bird species richness surveys

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0 Published in 2017 at "Ecological Indicators"

DOI: 10.1016/j.ecolind.2017.05.017

Abstract: This paper considers computer-assisted learning of sound spectra in environmental recordings to facilitate manual bird species identification. Today, a variety of automated methods have been successfully applied for acoustic recognition of specific bird species. These… read more here.

Keywords: negative matrix; bird; matrix factorisation; non negative ... See more keywords

Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation

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0 Published in 2020 at "Journal of biomedical informatics"

DOI: 10.1016/j.jbi.2020.103606

Abstract: Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing… read more here.

Keywords: using non; negative matrix; multimorbidity; multimorbidity patterns ... See more keywords

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

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1 Published in 2020 at "Scientific Reports"

DOI: 10.1038/s41598-020-65257-w

Abstract: Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate… read more here.

Keywords: negative matrix; muscle; factorisation; matrix factorisation ... See more keywords

Single channel blind source separation based on probabilistic matrix factorisation

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0 Published in 2017 at "Electronics Letters"

DOI: 10.1049/el.2017.2013

Abstract: A novel single channel blind source separation method based on probabilistic matrix factorisation (PMF) is proposed. Compared to the conventional non-negative matrix factorisation (NMF) employing Euclidean distance or Kullback–Leibler divergence, PMF uses the log posterior… read more here.

Keywords: source; source separation; single channel; matrix factorisation ... See more keywords

Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation

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0 Published in 2019 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2018.2859223

Abstract: Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the… read more here.

Keywords: matrix factorisation; relationship; pairwise; non negative ... See more keywords