Squintability and Other Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimization Choices

[2407.13663] Squintability and Other Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimization Choices Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Computation arXiv:2407.13663 (stat) [Submitted on 18 Jul 2024 ( v1 ), last revised 4 Jan 2026 (this version, v4)] Title: Squintability and Other Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimization Choices Authors: H. Sherry Zhang , Dianne Cook , Nicolas Langrené , Jessica Wai Yin Leung View a PDF of the paper titled Squintability and Other Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimization Choices, by H. Sherry Zhang and 3 other authors View PDF HTML (experimental) Abstract: The projection pursuit (PP) guided tour optimizes a criterion function, known as the PP index, to gradually reveal projections of interest from high-dimensional data through animation. Optimization of some PP indexes can be non-trivial, if they are non-smooth functions, or when the optimum has a small "squint angle", detectable only from close proximity. Here, measures for calculating the smoothness and squintability properties of the PP index are defined. These are used to investigate the performance of a recently introduced swarm-based algorithm, Jellyfish Search Optimizer (JSO), for optimizing PP indexes. The performance of JSO in detecting the target pattern (pipe shape) is compared with existing optimizers in PP. Additionally, JSO's performance on detecting the sine-wave shape is evaluated using different PP indexes (hence different smoothness and squintability) across various data dimensions (d = 4, 6, 8, 10, 12) and JSO hyper-parameters. We observe empirically that higher squintability improves the success rate of the PP index optimization, while smoothness has no significant effect. The JSO algorithm has been implemented in the R package, `tourr`, and functions to calculate smoothness and squintability measures are implemented in the `ferrn` package. Subjects: Computation (stat.CO) ; Neural and Evolutionary Computing (cs.NE) Cite as: arXiv:2407.13663 [stat.CO] (or arXiv:2407.13663v4 [stat.CO] for this version) https://doi.org/10.48550/arXiv.2407.13663 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of Computational and Graphical Statistics 35(3) 1305-1316 (2026) Related DOI : https://doi.org/10.1080/10618600.2026.2614076 Focus to learn more DOI(s) linking to related resources Submission history From: H.Sherry Zhang [ view email ] [v1] Thu, 18 Jul 2024 16:39:01 UTC (7,672 KB) [v2] Mon, 14 Oct 2024 02:22:51 UTC (5,918 KB) [v3] Sun, 9 Mar 2025 00:07:38 UTC (6,067 KB) [v4] Sun, 4 Jan 2026 17:32:50 UTC (5,677 KB) Full-text links: Access Paper: View a PDF of the paper titled Squintability and Other Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimization Choices, by H. 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