Interactive knowledge-based kernel PCA

Oglic, Dino; Paurat, Daniel; Gartner, Thomas

Abstract

Data understanding is an iterative process in which domain experts combine their knowledge with the data at hand to explore and confirm hypotheses. One important set of tools for exploring hypotheses about data are visualizations. Often, however, traditional, unsupervised dimensionality reduction algorithms are used for visualization. These tools allow for interaction, i.e., exploring different visualizations, only by means of manipulating some technical parameters of the algorithm. Therefore, instead of being able to intuitively interact with the visualization, domain experts have to learn and argue about these technical parameters. In this paper we propose a knowledge-based kernel PCA approach that allows for intuitive interaction with data visualizations. Each embedding direction is given by a non-convex quadratic optimization problem over an ellipsoid and has a globally optimal solution in the kernel feature space. A solution can be found in polynomial time using the algorithm presented in this paper. To facilitate direct feedback, i.e., updating the whole embedding with a sufficiently high frame-rate during interaction, we reduce the computational complexity further by incremental up- and down-dating. Our empirical evaluation demonstrates the flexibility and utility of this approach.

Citation

Oglic, D., Paurat, D., & Gartner, T. (2014). Interactive knowledge-based kernel PCA. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Machine Learning and Knowledge Discovery in Databases.European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II (501-516). https://doi.org/10.1007/978-3-662-44851-9_32

• Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
• Start Date: Sep 15, 2014
• End Date: Sep 19, 2014
• Acceptance Date: Jun 9, 2014
• Online Publication Date: Sep 15, 2014
• Publication Date: Sep 15, 2014
• Deposit Date: Feb 16, 2017
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 501-516
• Series Title: Lecture Notes in Computer Science
• Series Number: 8725
• Book Title: Machine Learning and Knowledge Discovery in Databases.European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II
• Chapter Number: 32
• ISBN: 978-3-662-44850-2
• DOI: https://doi.org/10.1007/978-3-662-44851-9_32
• Public URL: https://nottingham-repository.worktribe.com/output/1115528
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-662-44851-9_32