A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction

面向多元时间序列分类的用户友好基础模型适配器

User-friendly Foundation Model Adapters for Multivariate Time Series Classification

本文通过降维技术（如主成分分析和神经网络适配器）降低多元时间序列数据维度，使基础模型在标准GPU上高效运行，实现高达10倍加速和4.5倍数据集容纳量提升，且不牺牲性能。

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. However, to interpret the DR result for gaining useful insights from the data, it would take additional analysis effort such as identifying clusters and understanding their characteristics. While there are many automatic methods (e.g., density-based clustering methods) to identify clusters, effective methods for understanding a cluster's characteristics are still lacking. A cluster can be mostly characterized by its distribution of feature values.…

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical…

基于表示学习与复合变量构造的多元网络可视分析

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction

本文提出一种结合神经网络表示学习、复合变量构造与交互式可视化的多元网络可视分析工作流，用于提取网络结构与语义属性间的可解释关联，并通过社交媒体案例和专家反馈验证其有效性。

Uncertainty-Aware Principal Component Analysis

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertain…

深度变分多元信息瓶颈——一种变分损失框架

Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses

本研究提出深度变分多元信息瓶颈统一框架，推广多种变分降维方法，扩展beta-DVCCA并提出DVSIB新方法，实验验证其在多视图表示学习上的性能优势，为定制损失函数奠定基础。