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Nyström method with Kernel K-means++ samples as landmarks (2017)
Conference Proceeding
Oglic, D., & Gaertner, T. (2017). Nyström method with Kernel K-means++ samples as landmarks.

We investigate, theoretically and empirically, the effectiveness of kernel K-means++ samples as landmarks in the Nyström method for low-rank approximation of kernel matrices. Previous empirical studies (Zhang et al., 2008; Kumar et al.,2012) observe... Read More about Nyström method with Kernel K-means++ samples as landmarks.

Active search in intensionally specified structured spaces (2017)
Conference Proceeding
Oglic, D., Garnett, R., & Gärtner, T. (2017). Active search in intensionally specified structured spaces.

We consider an active search problem in intensionally specified structured spaces. The ultimate goal in this setting is to discover structures from structurally different partitions of a fixed but unknown target class. An example of such a process is... Read More about Active search in intensionally specified structured spaces.

Interactive knowledge-based kernel PCA (2014)
Conference Proceeding
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

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, tra... Read More about Interactive knowledge-based kernel PCA.