@inbook{
 type = {inbook},
 year = {2017},
 keywords = {Data visualization,Dimensionality reduction,Pairwise similarity},
 pages = {334-342},
 websites = {http://link.springer.com/10.1007/978-3-319-52277-7_41},
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 created = {2022-01-26T03:00:50.459Z},
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 last_modified = {2022-01-26T03:00:50.459Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rosero-Montalvo2017},
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 abstract = {This work presents a new interactive data visualization approach based on mixture of the outcomes of dimensionality reduction (DR) methods. Such a mixture is a weighted sum, whose weighting factors are defined by the user through a visual and intuitive interface. Additionally, the low-dimensional representation space produced by DR methods are graphically depicted using scatter plots powered via an interactive data-driven visualization. To do so, pairwise similarities are calculated and employed to define the graph to be drawn on the scatter plot. Our visualization approach enables the user to interactively combine DR methods while provided information about the structure of original data, making then the selection of a DR scheme more intuitive.},
 bibtype = {inbook},
 author = {Rosero-Montalvo, P. and Diaz, P. and Salazar-Castro, J. A. and Peña-Unigarro, D. F. and Anaya-Isaza, A. J. and Alvarado-Pérez, J. C. and Therón, R. and Peluffo-Ordóñez, D. H.},
 doi = {10.1007/978-3-319-52277-7_41},
 chapter = {Interactive Data Visualization Using Dimensionality Reduction and Similarity-Based Representations},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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