Multiview Reductive Decomposition

Justin D. Pearlman; Zimri Yaseen

doi:10.1117/12.309535

14 May 1998 Multiview Reductive Decomposition

Justin D. Pearlman, Zimri Yaseen

Proceedings Volume 3298, Visual Data Exploration and Analysis V; (1998) https://doi.org/10.1117/12.309535
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

Introduction: Data analysis for diagnostic purposes is considered from the standpoint of applying visualization to medical problem solving systems. We focus on efficacy and efficiency of decision-making based on visualization of reductive decompositions of time series image data. Methods: A multiview reductive decomposition presentation is a collection of reduced cardinality subsets of transformed data the results from decisions based on original data directly to optimal decisions are equivalent to those from the decomposition presentations and from a reconstructed approximation of the original data based on the presentations. Results: Three classes of decomposition are evaluated: interactive dynamic, fixed dynamic, and static. Dynamic change with time. Fixed dynamic presentations are suitable for videotape, while interactive require a computer. Methods for design and evaluation of novel presentations, and equations for analysis of error propagation are presented. Conclusions: Computed decomposition and presentation of time-series data offers substantive reductions in the expertise and time required to understand complex data sets. Visualization is intrinsic to the method, and is also useful for comparing different decompositions. The interdependence of non- orthogonal decompositions provides context and improves confidence tracking. Performance is enhanced by tailoring data visualization to the requirements of the problem-solving system.

Citation Download Citation

Justin D. Pearlman and Zimri Yaseen "Multiview Reductive Decomposition", Proc. SPIE 3298, Visual Data Exploration and Analysis V, (14 May 1998); https://doi.org/10.1117/12.309535

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