Visual feature fusion and its application to support unsupervised clustering tasks

Abstract

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.

HUMAP: Hierarchical Uniform Manifold Approximation and Projection

“…This is achieved by adding a term in the t-SNE's cost equation, controlling the trade-off between alignment and standard t-SNE optimization. VFF [9] also uses a trade-off parameter to control the alignment of projection in feature fusion tasks. However, VFF is not suitable for preservation of neighborhoods or local structures [4].…”

Teaching user-friendly web design: A case study on Zillow.com in the real estate industry

“…As much as designers attempt to separate and distinguish web features on a site, users inevitably conflate distinct concepts and/or features in their usage of a site like Zillow or many others as well. Hilasaca and Paulovich (2020) outline the critical importance of data gathering and calculation when it comes to this phenomenon: “The first step of our process is sampling. Since users employ the sample visualization to guide the feature fusion process, it is essential to have all possible data patterns from the different features represented.” Not only collecting said data, but in addition, comparing and contrasting visualization information is a key to continual optimization.…”

Exploring Neural Network Hidden Layer Activity Using Vector Fields

“…In this context, projections need to be aligned as best as possible, i.e., distances between points in projections must be as similar as possible to those in the original data, while also keeping projections as similar as possible with each other. Currently, there are three different approaches to generating aligned projections of multiple feature sets in literature: Dynamic t-SNE (Dt-SNE) [27], Visual Feature Fusion (VFF) [28], and the generic alignment model [7].…”