Abstract
This letter formally introduces the concept of interaction model (IM), which has been used either directly or tangentially in previous works but never defined. Broadly speaking, an IM consists of the use of a mixture of dimensionality reduction (DR) techniques within an interactive data visualization framework. The rationale of creating an IM is the need for simultaneously harnessing the benefit of several DR approaches to reach a data representation being intelligible and/or fitted to any user’s criterion. As a remarkable advantage, an IM naturally provides a generalized framework for designing both interactive DR approaches as well as readily-to-use data visualization interfaces. In addition to a comprehensive overview on basics of data representation and dimensionality reduction, the main contribution of this manuscript is the elegant definition of the concept of IM in mathematical terms.
Keywords
This work is supported by Yachay Tech and SDAS Research Group (http://www.sdas-group.com).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Some IM-based interfaces are available at https://sdas-group.com/gallery/.
References
Gou, J., Yang, Y., Yi, Z., Lv, J., Mao, Q., Zhan, Y.: Discriminative globality and locality preserving graph embedding for dimensionality reduction. Expert Syst. Appl. 144, 113079 (2020)
Lee, J.A., Peluffo-Ordóñez, D.H., Verleysen, M.: Multi-scale similarities in stochastic neighbour embedding: reducing dimensionality while preserving both local and global structure. Neurocomputing 169, 246–261 (2015)
Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press (2010)
Peluffo-Ordónez, D.H., Alvarado-Pérez, J.C., Lee, J.A., Verleysen, M., et al.: Geometrical homotopy for data visualization. In: European Symposium on Artificial Neural Networks (ESANN 2015). Computational Intelligence and Machine Learning (2015)
Salazar-Castro, J., Rosas-Narváez, Y., Pantoja, A., Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H.: Interactive interface for efficient data visualization via a geometric approach. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–6. IEEE (2015)
Rosero-Montalvo, P., et al.: Interactive data visualization using dimensionality reduction and similarity-based representations. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 334–342. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52277-7_41
Rosero-Montalvo, P.D., Peña-Unigarro, D.F., Peluffo, D.H., Castro-Silva, J.A., Umaquinga, A., Rosero-Rosero, E.A.: Data visualization using interactive dimensionality reduction and improved color-based interaction model. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2017. LNCS, vol. 10338, pp. 289–298. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59773-7_30. (Cited by 8)
Umaquinga-Criollo, A.C., Peluffo-Ordóñez, D.H., Rosero-Montalvo, P.D., Godoy-Trujillo, P.E., Benítez-Pereira, H.: Interactive visualization interfaces for big data analysis using combination of dimensionality reduction methods: a brief review. In: Basantes-Andrade, A., Naranjo-Toro, M., Zambrano Vizuete, M., Botto-Tobar, M. (eds.) TSIE 2019. AISC, vol. 1110, pp. 193–203. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37221-7_17
Amin, A., et al.: Cross-company customer churn prediction in telecommunication: a comparison of data transformation methods. Int. J. Inf. Manag. 46, 304–319 (2019)
Peluffo, D., Lee, J., Verleysen, M., Rodríguez-Sotelo, J., Castellanos-Domínguez, G.: Unsupervised relevance analysis for feature extraction and selection: a distance-based approach for feature relevance. In: International Conference on Pattern Recognition, Applications and Methods-ICPRAM (2014)
Cao, H., Bernard, S., Heutte, L., Sabourin, R.: Dissimilarity-based representation for radiomics applications. CoRR abs/1803.04460 (2018)
Zhong, G., Wang, L.N., Ling, X., Dong, J.: An overview on data representation learning: from traditional feature learning to recent deep learning. J. Finance Data Sci. 2(4), 265–278 (2016)
Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer, Heidelberg (2007). https://doi.org/10.1007/978-0-387-39351-3
Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer, Heidelberg (2005). https://doi.org/10.1007/0-387-28981-X
Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Generalized kernel framework for unsupervised spectral methods of dimensionality reduction. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 171–177. IEEE (2014)
Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Short review of dimensionality reduction methods based on stochastic neighbour embedding. In: Villmann, T., Schleif, F.-M., Kaden, M., Lange, M. (eds.) Advances in Self-Organizing Maps and Learning Vector Quantization. AISC, vol. 295, pp. 65–74. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07695-9_6
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
Zhang, Z., Wang, J.: MLLE: modified locally linear embedding using multiple weights. In: Advances in Neural Information Processing Systems, pp. 1593–1600 (2007)
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, pp. 857–864 (2003)
Ham, J., Lee, D.D., Mika, S., Schölkopf, B.: A kernel view of the dimensionality reduction of manifolds. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 47. ACM (2004)
Acknowledgment
The authors acknowledge to the research project “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by Agreement No. 180 November 1st, 2016 by VIPRI from Universidad de Nariño.
As well, authors thank the valuable support given by the SDAS Research Group (www.sdas-group.com).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ortega-Bustamante, M.C. et al. (2020). Introducing the Concept of Interaction Model for Interactive Dimensionality Reduction and Data Visualization. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-58802-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58801-4
Online ISBN: 978-3-030-58802-1
eBook Packages: Computer ScienceComputer Science (R0)