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Found 15 papers, 5 papers with code
Network Comparison with Interpretable Contrastive Network Representation Learning
This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another.
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties
Scaling methods have long been utilized to simplify and cluster high-dimensional data.
Interactive Dimensionality Reduction for Comparative Analysis
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.
Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction
Multivariate networks are commonly found in real-world data-driven applications.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data.
A Visual Analytics Framework for Reviewing Streaming Performance Data
To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
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.
A Visual Analytics Framework for Contrastive Network Analysis
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
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 Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts
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.
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data
In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects.
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data.
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks
Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions.
