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Found speech and humans in the loop : Ways to gain insight into large quantities of speech
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| 2 |
Development of Gaussian Learning Algorithms for Early Detection of Alzheimer's Disease
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In: FIU Electronic Theses and Dissertations (2020)
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How to visualize high-dimensional data: a roadmap
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In: Journal of Data Mining and Digital Humanities, Vol Special issue on Visualisations in Historical Linguistics (2020) (2020)
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Abstract:
International audience Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis.
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Keyword:
[shs]humanities and social sciences; AZ20-999; Bibliography. Library science. Information resources; cluster analysis; data visualization; dimensionality reduction; high dimensionality; History of scholarship and learning. The humanities; multivariate data; Z
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URL: https://doaj.org/article/7655036ba85642e9901cfbbfc0a6bf5e
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| 4 |
An Empirical Study of Word Embedding Dimensionality Reduction ...
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An Empirical Study of Word Embedding Dimensionality Reduction ...
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Dimensionality reduction methods for machine translation quality estimation
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C.: EdgeMaps: Visualizing Explicit and Implicit Relations
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In: http://www.mariandoerk.de/edgemaps/vda2011.pdf (2011)
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Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology
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In: http://www.cs.toronto.edu/~gh/Courses/2528/Readings/2002f/Lai+Wu.pdf (2002)
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Dimensionality reduction of electropalatographic data using latent variable models
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EdgeMaps: Visualizing Explicit and Implicit Relations
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In: http://www.imaging.org/ist/publications/reporter/articles/REP26_2_EI2011_DOERK_7868_14.pdf
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EdgeMaps: Visualizing Explicit and Implicit Relations
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In: http://innovis.cpsc.ucalgary.ca/innovis/uploads/Publications/Publications/Doerk2011VDA.pdf
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