6
2013.
Supervised PCA for Interactive Data Analysis
We investigate a novel approach for intuitive interaction with a data set for explorative data analysis. The key idea is that a user can directly interact with a two or three dimensional embedding of the data and actively place data points to desired locations. To achieve this, we propose a variant of semisupervised kernel PCA which respects the placement of control points and maximizes the variance of the unlabelled data along the ‘directions’ of the embedding.