User-guided Dimensionality Reduction Ensembles

摘要

Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.