@inproceedings{
 title = {Data visualization using interactive dimensionality reduction and improved color-based interaction model},
 type = {inproceedings},
 year = {2017},
 keywords = {Color-based model,Data visualization,Dimensionality reduction,Pairwise similarity},
 pages = {289-298},
 volume = {10338 LNCS},
 websites = {https://link.springer.com/chapter/10.1007%2F978-3-319-59773-7_30},
 publisher = {Springer Verlag},
 id = {dd754d5c-5271-3989-af78-8a51ef09dff9},
 created = {2022-01-26T03:00:05.670Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:00:05.670Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This work presents an improved interactive data visualization interface based on a mixture of the outcomes of dimensionality reduction (DR) methods. Broadly, it works as follows: The user can input the mixture weighting factors through a visual and intuitive interface with a primary-light-colors-based model (Red, Green, and Blue). By design, such a mixture is a weighted sum of the color tone. 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 simultaneously be drawn over the scatter plot. Our interface enables the user to interactively combine DR methods by the human perception of color, while providing information about the structure of original data. Then, it makes the selection of a DR scheme more intuitive -even for non-expert users.},
 bibtype = {inproceedings},
 author = {Rosero-Montalvo, P. D. and Peña-Unigarro, D. F. and Peluffo, D. H. and Castro-Silva, J. A. and Umaquinga, A. and Rosero-Rosero, E. A.},
 doi = {10.1007/978-3-319-59773-7_30},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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