Subjects classification from high-dimensional and small-sample size datasets using a strategy based on Clustering Variables around Latent Components (CLV) method
High-dimensional complex systems can be studied through multivariate analysis, as Principal Component Analysis, however large samples of observations frequently are needed for it. Here it is examined a method for small samples based on clustering variables around latent variables (CLV) to subject cl...
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| Main Author | |
|---|---|
| Format | Journal Article |
| Language | English |
| Published |
14.06.2017
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| Subjects | |
| Online Access | Get full text |
| DOI | 10.48550/arxiv.1706.04633 |
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| Abstract | High-dimensional complex systems can be studied through multivariate
analysis, as Principal Component Analysis, however large samples of
observations frequently are needed for it. Here it is examined a method for
small samples based on clustering variables around latent variables (CLV) to
subject classification in two presumed groups. For it, a predictive model was
developed to generate datasets with two groups of cases whose variables show
randomness features (up to 30% of variables manifest difference between groups,
and up to 7% of those are correlated between them). The method recovered the
information of the latent factors to classify the subjects with 80 to 95% of
agreement, with positive relationship between the classifier precision and the
rate [number of variables / number of subjects]. |
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| AbstractList | High-dimensional complex systems can be studied through multivariate
analysis, as Principal Component Analysis, however large samples of
observations frequently are needed for it. Here it is examined a method for
small samples based on clustering variables around latent variables (CLV) to
subject classification in two presumed groups. For it, a predictive model was
developed to generate datasets with two groups of cases whose variables show
randomness features (up to 30% of variables manifest difference between groups,
and up to 7% of those are correlated between them). The method recovered the
information of the latent factors to classify the subjects with 80 to 95% of
agreement, with positive relationship between the classifier precision and the
rate [number of variables / number of subjects]. |
| Author | Abramov, Dimitri Marques |
| Author_xml | – sequence: 1 givenname: Dimitri Marques surname: Abramov fullname: Abramov, Dimitri Marques |
| BackLink | https://doi.org/10.48550/arXiv.1706.04633$$DView paper in arXiv |
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| Snippet | High-dimensional complex systems can be studied through multivariate
analysis, as Principal Component Analysis, however large samples of
observations... |
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| SubjectTerms | Statistics - Applications |
| Title | Subjects classification from high-dimensional and small-sample size datasets using a strategy based on Clustering Variables around Latent Components (CLV) method |
| URI | https://arxiv.org/abs/1706.04633 |
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