By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence [
Dimensionality reduction (DimRed)
DimRed is a key tool for artificial intelligence tasks—more specifically machine learning—that involve high dimensional data sets. The aim of DimRed approaches is to extract lower dimensional, relevant information (called embedded data) from high-dimensional input data so that the performance of a pattern recognition system is improved and/or the data visualization becomes more intelligible. Principal component analysis (PCA) and classical multidimensional scaling (CMDS) are two classical DimRed approaches based on variance and distance preservation criteria, respectively [
Likewise, since the rows of the normalized similarity matrix can be interpreted as probability distributions, methods based on divergences have emerged. Due to its probabilistic connotation, the most representative among such methods is called stochastic neighbor embedding (SNE) [
Information visualization (InfoVis)
Recent analyses have indicated that DimRed should reach two goals: (1) ensure that data points that are neighbors in the original space remain neighbors in the embedded space and (2) guarantee that two data points are shown as neighbors in the embedded space only if they are neighbors in the original space. In the context of information retrieval, these two goals can be seen as precision and recall measures, respectively. Although clearly conflictive, the compromise between precision and recall defines the performance of the DimRed method.
Furthermore, since DimRed methods are often developed under predetermined design parameters and pre-established optimization criterion, they still lack properties such as user interaction and controllability. Such properties are characteristic of information visualization (InfoVis) procedures. The field of InfoVis aims to develop graphical ways to represent data so that information can be more usable and intelligible for users [
Integration between InfoVis and DimRed
The main goal of this research area is to link the field of DimRed with that of InfoVis to harness the special properties of the latter within DimRed frameworks. Therefore, controllability and interactivity properties are of great interest because they may make the DimRed outcomes significantly more understandable and tractable for (not necessarily expert) users. Particularly, these two properties provide users with leeway to explore and select the best ways to represent data. In other words, the goal of the aforementioned integration is to develop a DimRed framework that facilitates an interactive and quick visualization of data representations that make DimRed outcomes more intelligible and allow users to modify data views according to their needs in an affordable fashion [
Future studies in the field of InfoVis-DimRed integration should address the following open issues: