var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/baf5476f-9730-35ad-96cf-38963c8e0da9?jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/baf5476f-9730-35ad-96cf-38963c8e0da9?jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/baf5476f-9730-35ad-96cf-38963c8e0da9?jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2026\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Multi-ROI Multimodal 3D Vision Transformer for Alzheimer’s Disease Classification with Attention-Based Interpretability.\n \n \n \n \n\n\n \n Castro-Silva, J., A.; Moreno-García, M., N.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Applied Sciences, 16(11). 2026.\n \n\n\n\n
\n\n\n\n \n \n \"Multi-ROIWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Multi-ROI Multimodal 3D Vision Transformer for Alzheimer’s Disease Classification with Attention-Based Interpretability},\n type = {article},\n year = {2026},\n volume = {16},\n websites = {https://www.mdpi.com/2076-3417/16/11/5705},\n id = {4f503773-3609-37b0-9cd9-2ed0aec45f92},\n created = {2026-07-24T03:18:12.261Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:18:12.261Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n abstract = {Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within a unified attention-based framework. The proposed approach leverages anatomically guided multi-region-of-interest (ROI) decomposition to focus on disease-relevant brain structures, including the hippocampus, entorhinal cortex, fornix, and major cortical lobes. Each ROI is encoded using 3D tubelet embeddings, while clinical and volumetric features are transformed into feature-wise tokens, enabling seamless multimodal fusion through self-attention mechanisms. A hemisphere-aware selection strategy is introduced to identify the most discriminative ROI representations, enhancing both performance and interpretability. The model is evaluated on a merged multi-cohort dataset combining ADNI, AIBL, and OASIS using a 7-fold cross-validation protocol. Experimental results demonstrate that the proposed method achieves high classification performance, reaching an accuracy of 97.62% and an AUC of 0.9940, outperforming single-modality and whole-brain baselines. Furthermore, attention-based analysis provides interpretable insights into the relative importance of clinical and neuroanatomical features, revealing consistency with established AD biomarkers. These findings highlight the effectiveness of multimodal integration and ROI-based representation for robust and explainable AD classification.},\n bibtype = {article},\n author = {Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/app16115705},\n journal = {Applied Sciences},\n number = {11}\n}
\n
\n\n\n
\n Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within a unified attention-based framework. The proposed approach leverages anatomically guided multi-region-of-interest (ROI) decomposition to focus on disease-relevant brain structures, including the hippocampus, entorhinal cortex, fornix, and major cortical lobes. Each ROI is encoded using 3D tubelet embeddings, while clinical and volumetric features are transformed into feature-wise tokens, enabling seamless multimodal fusion through self-attention mechanisms. A hemisphere-aware selection strategy is introduced to identify the most discriminative ROI representations, enhancing both performance and interpretability. The model is evaluated on a merged multi-cohort dataset combining ADNI, AIBL, and OASIS using a 7-fold cross-validation protocol. Experimental results demonstrate that the proposed method achieves high classification performance, reaching an accuracy of 97.62% and an AUC of 0.9940, outperforming single-modality and whole-brain baselines. Furthermore, attention-based analysis provides interpretable insights into the relative importance of clinical and neuroanatomical features, revealing consistency with established AD biomarkers. These findings highlight the effectiveness of multimodal integration and ROI-based representation for robust and explainable AD classification.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2024\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration.\n \n \n \n \n\n\n \n Castro-Silva., J., A.; Moreno-García., M.; Guachi-Guachi., L.; and Peluffo-Ordóñez., D., H.\n\n\n \n\n\n\n In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, pages 453-460, 2024. SciTePress\n \n\n\n\n
\n\n\n\n \n \n \"InstanceWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},\n type = {inproceedings},\n year = {2024},\n pages = {453-460},\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654},\n publisher = {SciTePress},\n institution = {INSTICC},\n id = {383a3523-bcf3-3875-84e1-040a82e12726},\n created = {2026-07-24T03:18:03.894Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:18:03.894Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {icpram24},\n source_type = {conference},\n private_publication = {false},\n abstract = {Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.},\n bibtype = {inproceedings},\n author = {Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.},\n doi = {10.5220/0012469600003654},\n booktitle = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM}\n}
\n
\n\n\n
\n Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer.\n \n \n \n \n\n\n \n Castro-Silva, J., A.; Moreno-García, M., N.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Mathematics, 12(17): 2720. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"MultipleWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer},\n type = {article},\n year = {2024},\n pages = {2720},\n volume = {12},\n websites = {https://www.mdpi.com/2227-7390/12/17/2720},\n publisher = {MDPI},\n id = {1d0c5349-6545-3b9b-9cab-945bfc428fa9},\n created = {2026-07-24T03:18:07.903Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:18:07.903Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {castro2024multiple},\n source_type = {article},\n private_publication = {false},\n abstract = {The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.},\n bibtype = {article},\n author = {Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H},\n journal = {Mathematics},\n number = {17}\n}
\n
\n\n\n
\n The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data.\n \n \n \n \n\n\n \n Castro-Silva, J., A.; Moreno-García, M., N.; Guachi-Guachi, L.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Heliyon, 10(19): e37552. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"NovelWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data},\n type = {article},\n year = {2024},\n keywords = {Alzheimer's disease,Deep learning,Hippocampus,Instance selection},\n pages = {e37552},\n volume = {10},\n websites = {https://www.cell.com/heliyon/fulltext/S2405-8440(24)13583-9},\n id = {6a32fb4a-a5c6-3d1a-a301-42d4e144d3b5},\n created = {2026-07-24T03:18:09.971Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:18:09.971Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {CASTROSILVA2024e37552},\n source_type = {article},\n private_publication = {false},\n abstract = {The quantity and quality of a dataset play a crucial role in the performance of prediction models. Increasing the amount of data increases the computational requirements and can introduce negligible variations, outliers, and noise. These significantly impact the model performance. Thus, instance selection techniques are crucial for building prediction models with informative data, reducing the dataset size, improving performance, and minimizing computational costs. This study proposed a novel methodology for identifying the most informative two-dimensional slices derived from magnetic resonance imaging (MRI) to study Alzheimer's disease. The efficacy of our methodology was attributable to a hippocampus-centered analysis using data from multiple atlases. The methodology was evaluated by constructing convolutional neural networks to identify Alzheimer's disease, using a consolidated dataset constructed from three standard datasets: Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing, and Open Access Series of Imaging Studies. The proposed methodology demonstrated a commendable subject-level classification accuracy of approximately (95.00%) when distinguishing between normal cognition and Alzheimer's},\n bibtype = {article},\n author = {Castro-Silva, Juan A and Moreno-García, María N and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego H},\n doi = {https://doi.org/10.1016/j.heliyon.2024.e37552},\n journal = {Heliyon},\n number = {19}\n}
\n
\n\n\n
\n The quantity and quality of a dataset play a crucial role in the performance of prediction models. Increasing the amount of data increases the computational requirements and can introduce negligible variations, outliers, and noise. These significantly impact the model performance. Thus, instance selection techniques are crucial for building prediction models with informative data, reducing the dataset size, improving performance, and minimizing computational costs. This study proposed a novel methodology for identifying the most informative two-dimensional slices derived from magnetic resonance imaging (MRI) to study Alzheimer's disease. The efficacy of our methodology was attributable to a hippocampus-centered analysis using data from multiple atlases. The methodology was evaluated by constructing convolutional neural networks to identify Alzheimer's disease, using a consolidated dataset constructed from three standard datasets: Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing, and Open Access Series of Imaging Studies. The proposed methodology demonstrated a commendable subject-level classification accuracy of approximately (95.00%) when distinguishing between normal cognition and Alzheimer's\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2022\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI.\n \n \n \n \n\n\n \n Castro-Silva., J.; Moreno-García., M.; Guachi-Guachi., L.; and Peluffo-Ordóñez., D.\n\n\n \n\n\n\n In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,, pages 330-337, 2022. SciTePress\n \n\n\n\n
\n\n\n\n \n \n \"InstanceWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI},\n type = {inproceedings},\n year = {2022},\n pages = {330-337},\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122},\n publisher = {SciTePress},\n institution = {INSTICC},\n id = {4b14132c-37af-3318-a472-bdb72633e02d},\n created = {2026-07-24T03:17:49.766Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:17:49.766Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {icpram22},\n source_type = {conference},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Castro-Silva., J and Moreno-García., M and Guachi-Guachi., Lorena and Peluffo-Ordóñez., D},\n doi = {10.5220/0010900100003122},\n booktitle = {Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,}\n}
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Data visualization using interactive dimensionality reduction and improved color-based interaction model.\n \n \n \n \n\n\n \n Rosero-Montalvo, P., D.; Peña-Unigarro, D., F.; Peluffo, D., H.; Castro-Silva, J., A.; Umaquinga, A.; and Rosero-Rosero, E., A.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10338 LNCS, pages 289-298, 2017. Springer Verlag\n \n\n\n\n
\n\n\n\n \n \n \"DataWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Data visualization using interactive dimensionality reduction and improved color-based interaction model},\n type = {inproceedings},\n year = {2017},\n keywords = {Color-based model,Data visualization,Dimensionality reduction,Pairwise similarity},\n pages = {289-298},\n volume = {10338 LNCS},\n websites = {https://link.springer.com/chapter/10.1007%2F978-3-319-59773-7_30},\n publisher = {Springer Verlag},\n id = {f025482f-80fc-3a5d-b403-e3bdd7344256},\n created = {2026-07-24T03:17:24.094Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:17:24.094Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n 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.},\n bibtype = {inproceedings},\n 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.},\n doi = {10.1007/978-3-319-59773-7_30},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}
\n
\n\n\n
\n 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.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Interactive Data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations.\n \n \n \n \n\n\n \n Peña-Unigarro, D., F.; Rosero-Montalvo, P.; Revelo-Fuelagán, E., J.; Castro-Silva, J., A.; Alvarado-Pérez, J., C.; Therón, R.; Ortega-Bustamante, C., M.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 461-469. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"LectureWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2017},\n keywords = {Data visualization,Dimensionality reduction,Pairwise dissimilarity},\n pages = {461-469},\n websites = {http://link.springer.com/10.1007/978-3-319-68935-7_50},\n id = {846f348a-0bf4-30e9-8e21-f46efd394290},\n created = {2026-07-24T03:18:01.427Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:18:01.427Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Pena-Unigarro2017},\n private_publication = {false},\n abstract = {This work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.},\n bibtype = {inbook},\n author = {Peña-Unigarro, D. F. and Rosero-Montalvo, P. and Revelo-Fuelagán, E. J. and Castro-Silva, J. A. and Alvarado-Pérez, J. C. and Therón, R. and Ortega-Bustamante, C. M. and Peluffo-Ordóñez, D. H.},\n doi = {10.1007/978-3-319-68935-7_50},\n chapter = {Interactive Data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}
\n
\n\n\n
\n This work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Estudio comparativo de métodos espectrales para reducción de la dimensionalidad: LDA versus PCA . Comparative study between spectral methods for dimension reduction LDA versus PCA.\n \n \n \n \n\n\n \n Anaya-Isaza, A., J.; Peluffo-Ordoñez, D., H.; Alvarado-Pérez, J., C.; Ivan-Rios, J.; Castro-Silva, J., A.; Rosero-Montalvo, P., D.; Peña-Unigarro, D., F.; C., J., S., A.; and Umaquinga-Criollo, A., C.\n\n\n \n\n\n\n INCISCOS 2016 International Conference on Information Systems and Computer Science. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"EstudioWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Estudio comparativo de métodos espectrales para reducción de la dimensionalidad: LDA versus PCA . Comparative study between spectral methods for dimension reduction LDA versus PCA.},\n type = {article},\n year = {2016},\n keywords = {Análisis de componentes principales,Análisis discriminante lineal,Aprendizaje de máquina,Clasificación lineal,Clasificación supervisada,Métodos de reducción de la dimensión},\n websites = {http://ingenieria.ute.edu.ec/conferencias/index.php/inciscos/2016/paper/view/31},\n id = {4c3f99f1-d045-322f-a9d6-6eff9637d40e},\n created = {2026-07-24T03:17:53.336Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {baf5476f-9730-35ad-96cf-38963c8e0da9},\n last_modified = {2026-07-24T03:17:53.336Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Anaya-Isaza2016},\n private_publication = {false},\n abstract = {Este trabajo presenta un estudio comparativo con métodos de reducción de la dimensión lineal,tales como: Análisis de Componentes Principales &Análisis Discriminante Lineal. El estudio pretende determinar, bajo criterios de objetividad, cuál de estas técnicas obtiene el mejor resultado de separabilidad entre clases. Para la validación experimental se utilizan dos bases de datos, del repositorio científico(UC Irvine Machine Learning Repository), para dar tratamiento a los atributos del data-set en función deconfirmar visualmente la calidad de los resultados obtenidos. Las inmersiones obtenidas son analizadas, para realizar una comparación de resultados del embedimiento representados con RNX(K), que permite evaluar el área bajo la curva, del cual asume una mejor representación en una topología global o localque posteriormente generalos gráficos de visualización en un espacio de menor dimensión, para observar la separabilidad entre clases conservando la estructura global de los datos.},\n bibtype = {article},\n author = {Anaya-Isaza, Andrés J and Peluffo-Ordoñez, Diego H and Alvarado-Pérez, Juan C and Ivan-Rios, Jorge and Castro-Silva, Juan A and Rosero-Montalvo, Paul D and Peña-Unigarro, Diego F and C., Jose Salazar-Castrojuan A and Umaquinga-Criollo, Ana C},\n journal = {INCISCOS 2016 International Conference on Information Systems and Computer Science}\n}
\n
\n\n\n
\n Este trabajo presenta un estudio comparativo con métodos de reducción de la dimensión lineal,tales como: Análisis de Componentes Principales &Análisis Discriminante Lineal. El estudio pretende determinar, bajo criterios de objetividad, cuál de estas técnicas obtiene el mejor resultado de separabilidad entre clases. Para la validación experimental se utilizan dos bases de datos, del repositorio científico(UC Irvine Machine Learning Repository), para dar tratamiento a los atributos del data-set en función deconfirmar visualmente la calidad de los resultados obtenidos. Las inmersiones obtenidas son analizadas, para realizar una comparación de resultados del embedimiento representados con RNX(K), que permite evaluar el área bajo la curva, del cual asume una mejor representación en una topología global o localque posteriormente generalos gráficos de visualización en un espacio de menor dimensión, para observar la separabilidad entre clases conservando la estructura global de los datos.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);