A novel color-based data visualization approach using a circular interaction model and dimensionality reduction. Salazar-Castro, J., A., Rosero-Montalvo, P., D., Peña-Unigarro, D., F., Umaquinga-Criollo, A., C., Castillo-Marrero, Z., Revelo-Fuelagán, E., J., Peluffo-Ordóñez, D., H., & Castellanos-Domínguez, C., G. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10878 LNCS, pages 557-567, 2018.
abstract   bibtex   
Dimensionality reduction (DR) methods are able to produce low-dimensional representations of an input data sets which may become intelligible for human perception. Nonetheless, most existing DR approaches lack the ability to naturally provide the users with the faculty of controlability and interactivity. In this connection, data visualization (DataVis) results in an ideal complement. This work presents an integration of DR and DataVis through a new approach for data visualization based on a mixture of DR resultant representations while using visualization principle. Particularly, the mixture is done through a weighted sum, whose weighting factors are defined by the user through a novel interface. The interface’s concept relies on the combination of the color-based and geometrical perception in a circular framework so that the users may have a at hand several indicators (shape, color, surface size) to make a decision on a specific data representation. Besides, pairwise similarities are plotted as a non-weighted graph to include a graphic notion of the structure of input data. Therefore, the proposed visualization approach enables the user to interactively combine DR methods, while providing information about the structure of original data, making then the selection of a DR scheme more intuitive.
@inproceedings{
 title = {A novel color-based data visualization approach using a circular interaction model and dimensionality reduction},
 type = {inproceedings},
 year = {2018},
 identifiers = {[object Object]},
 keywords = {Data visualization,Dimensionality reduction,Interactive interface,Pairwise similarity},
 pages = {557-567},
 volume = {10878 LNCS},
 id = {57126ceb-822c-3c23-a0c8-dc8d59175ee2},
 created = {2018-06-13T07:54:08.290Z},
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 last_modified = {2021-10-05T13:36:21.046Z},
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 abstract = {Dimensionality reduction (DR) methods are able to produce low-dimensional representations of an input data sets which may become intelligible for human perception. Nonetheless, most existing DR approaches lack the ability to naturally provide the users with the faculty of controlability and interactivity. In this connection, data visualization (DataVis) results in an ideal complement. This work presents an integration of DR and DataVis through a new approach for data visualization based on a mixture of DR resultant representations while using visualization principle. Particularly, the mixture is done through a weighted sum, whose weighting factors are defined by the user through a novel interface. The interface’s concept relies on the combination of the color-based and geometrical perception in a circular framework so that the users may have a at hand several indicators (shape, color, surface size) to make a decision on a specific data representation. Besides, pairwise similarities are plotted as a non-weighted graph to include a graphic notion of the structure of input data. Therefore, the proposed visualization approach enables the user to interactively combine DR methods, while providing information about the structure of original data, making then the selection of a DR scheme more intuitive.},
 bibtype = {inproceedings},
 author = {Salazar-Castro, Jose Alejandro and Rosero-Montalvo, Paul D. and Peña-Unigarro, Diego Fernando and Umaquinga-Criollo, Ana Cristina and Castillo-Marrero, Zenaida and Revelo-Fuelagán, Edgardo Javier and Peluffo-Ordóñez, Diego Hernán and Castellanos-Domínguez, César Germán},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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