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Factor analysis is a multivariate technique designed to analyze correlations among many observed variables and to explore latent factors.
The resulting algorithm, which we term multiple activity model annealed particle filtering (MAM-APF), is able to dynamically vary the scope of its search effort, using a small number of particles to explore latent pose spaces and a large number of particles to explore the full pose space.
Principal axis factoring was undertaken to explore latent constructs and to assess the dimensionality of this scale.
Chaley et al. [ 30] applied a mutual information criterion to explore latent periodicity in sequence data using Z-scores based on mutual information.
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We aimed to explore the latent structure of the CES-D (Turkish version) as earlier research has described different factor structures for this scale.
Two LVM approaches can be used to explore the latent structure of this slice: using (1) discrete or (2) continuous LV's.
We aimed to explore possible latent factors in the pool of POSM questions and reliability of these factors in order to better inform future use and reporting of the POSM questions.
An exploratory factor analysis (EFA) was therefore indicated to explore the latent factor structure of the 96 scored questions, reduce the overlap between domain content and ensure common variation of items within a domain.
In addition to the scenarios, we explored latent sets of attitudes and ideas operating within the sample, i.e., what we here call visions.
Wallach [12] proposed a combination of bigram and LDA models (the bigram topic model) and achieved a significant performance improvement on perplexity by exploring latent semantics following different context words.
Exploring latent profility is beyond the scope of the present article.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com