Visual Analytics Using Tensor Unified Linear Comparative Analysis
This addresses the limitation of existing tensor decomposition methods in supporting flexible comparative analysis for analysts in fields like visual analytics, though it appears incremental as an extension of an existing DR method.
The paper tackles the problem of comparing tensors for complex data analysis by introducing TULCA, a tensor decomposition method that integrates discriminant analysis and contrastive learning, enabling flexible comparative analysis and visualization, with demonstrated efficacy in computational evaluations and case studies including supercomputer log data.
Comparing tensors and identifying their (dis)similar structures is fundamental in understanding the underlying phenomena for complex data. Tensor decomposition methods help analysts extract tensors' essential characteristics and aid in visual analytics for tensors. In contrast to dimensionality reduction (DR) methods designed only for analyzing a matrix (i.e., second-order tensor), existing tensor decomposition methods do not support flexible comparative analysis. To address this analysis limitation, we introduce a new tensor decomposition method, named tensor unified linear comparative analysis (TULCA), by extending its DR counterpart, ULCA, for tensor analysis. TULCA integrates discriminant analysis and contrastive learning schemes for tensor decomposition, enabling flexible comparison of tensors. We also introduce an effective method to visualize a core tensor extracted from TULCA into a set of 2D visualizations. We integrate TULCA's functionalities into a visual analytics interface to support analysts in interpreting and refining the TULCA results. We demonstrate the efficacy of TULCA and the visual analytics interface with computational evaluations and two case studies, including an analysis of log data collected from a supercomputer.