1 research outputs found
Visual Feature Fusion and its Application to Support Unsupervised Clustering Tasks
On visual analytics applications, the concept of putting the user on the loop
refers to the ability to replace heuristics by user knowledge on machine
learning and data mining tasks. On supervised tasks, the user engagement occurs
via the manipulation of the training data. However, on unsupervised tasks, the
user involvement is limited to changes in the algorithm parametrization or the
input data representation, also known as features. Depending on the application
domain, different types of features can be extracted from the raw data.
Therefore, the result of unsupervised algorithms heavily depends on the type of
employed feature. Since there is no perfect feature extractor, combining
different features have been explored in a process called feature fusion. The
feature fusion is straightforward when the machine learning or data mining task
has a cost function. However, when such a function does not exist, user support
for combination needs to be provided otherwise the process is impractical. In
this paper, we present a novel feature fusion approach that uses small data
samples to allows users not only to effortless control the combination of
different feature sets but also to interpret the attained results. The
effectiveness of our approach is confirmed by a comprehensive set of
qualitative and quantitative tests, opening up different possibilities of
user-guided analytical scenarios not covered yet. The ability of our approach
to providing real-time feedback for the feature fusion is exploited on the
context of unsupervised clustering techniques, where the composed groups
reflect the semantics of the feature combination.Comment: 15 pages, 21 Figure