invsus

Invsus is a term used in discussions of data visualization and inverse problems to denote a theoretical framework for studying the reconstructability of original data from visual summaries. It treats a visualization as a mapping from data space to a visual representation and investigates under what conditions the inverse mapping from visuals back to data can be defined or approximated, along with the implications for information loss and fidelity.

Origin and terminology: The name invsus is a contraction of inverse visualization and synthesis. It appears primarily in theoretical writeups and speculative discussions rather than as a standardized methodology, and its precise definitions vary across authors.

Key concepts: Core concerns include reconstructability, inversion stability, and information loss. Privacy implications arise when inversion reveals sensitive details. Methods discussed under invsus include regularization, prior-informed reconstruction, and the use of generative models to constrain plausible reconstructions. Evaluation typically uses reconstruction error, perceptual similarity, and task-based performance.

Applications and relevance: Researchers examine how visual summaries of data, such as charts or aggregated visuals, may leak underlying data, informing privacy-by-design and visualization design choices. invsus also provides a framework for comparing different visualization techniques by their invertibility and the fidelity of potential reconstructions.

Limitations and status: As a conceptual framework, invsus lacks a single formal definition or universally accepted metrics. Its treatment is highly context-dependent, drawing on ideas from inverse problems, information theory, and privacy-preserving data analysis. It remains primarily a theoretical construct used to frame discussions about data representation and data reconstruction.