However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups.
Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain.
Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data.
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
Scaling methods have long been utilized to simplify and cluster high-dimensional data.
Contrastive learning (CL) is an emerging analysis approach that aims to discover unique patterns in one dataset relative to another.
Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data.
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.