Procrustes-based distances for exploring between-matrices similarity
نویسندگان
چکیده
Abstract The statistical shape analysis called Procrustes minimizes the Frobenius distance between matrices by similarity transformations. method returns a set of optimal orthogonal matrices, which project each matrix into common space. This manuscript presents two types distances derived from for exploring between-matrices similarity. first one focuses on residuals analysis, i.e., residual-based metric. In contrast, second exploits fitted rotational-based Thanks to these distances, similarity-based techniques such as multidimensional scaling can be applied visualize and explore patterns similarities among observations. proposed result in being helpful functional magnetic resonance imaging (fMRI) data analysis. brain activation measured over space time represented matrix. sample subjects—i.e., matrices—revealed groups individuals sharing neural activation. Finally, is useful several contexts when aim analyze high-dimensional affected misalignment.
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ژورنال
عنوان ژورنال: Statistical Methods and Applications
سال: 2023
ISSN: ['1613-981X', '1618-2510']
DOI: https://doi.org/10.1007/s10260-023-00689-y