Generalized scale behavior and renormalization group for data analysis

نویسندگان

چکیده

Some recent results showed that renormalization group can be considered as a promising framework to address open issues in data analysis. In this work, we focus on one of these aspects, closely related principal component analysis for the case large dimensional sets with covariance having nearly continuous spectrum. case, distinction between "noise-like" and "non-noise" modes becomes arbitrary an challenge standard methods. Observing both search simplification systems involving many degrees freedom, aim use argument clarify turning point noise information modes. The analogy coarse-graining has been investigated [Journal Statistical Physics,167, Issue 3-4, pp 462-475, (2017)], from perturbative framework, implementation real by same authors procedure may reflect more than simple formal analogy. particular, separation sampling controlled non-Gaussian fixed point, reminiscent behaviour critical systems. our analysis, go beyond using nonperturbative techniques investigate points propose deeper formalism allowing going power-law assumptions explicit computations.

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ژورنال

عنوان ژورنال: Journal of Statistical Mechanics: Theory and Experiment

سال: 2022

ISSN: ['1742-5468']

DOI: https://doi.org/10.1088/1742-5468/ac52a6