The classification analysis based on the geometric feature of high-dimensional data is called geometrical quadratic discriminant analysis (GQDA). We create new ...
Downloadable (with restrictions)! We consider the quadratic classification for high-dimensional data under the strongly spiked eigenvalue (SSE) model.
Mar 1, 2022 · The classification analysis based on the geometric feature of high-dimensional data is called geometrical quadratic discriminant analysis (GQDA) ...
The classification analysis based on the geometric feature of high-dimensional data is called geometrical quadratic discriminant analysis (GQDA). We create new ...
Geometric classifiers for high-dimensional noisy data · List of references · Publications that cite this publication.
Geometric classifiers for high-dimensional noisy data. Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate Analysis. ISHII Aki; ,; YATA ...
Mar 7, 2024 · We propose a model for data classification using isolated quantum-level systems or else qudits. The procedure consists of an encoding phase.
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Apr 9, 2021 · Geometric graphs from data can be used in deep learning to improve classification. Optimized graphs align the data to the class labels and enhance class ...
Geometric classifiers for high-dimensional noisy data (Editor's invited paper). Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate ...
This is rarely the case in practice. This project focuses on building the foundations for two extensions to classic geometric settings pertinent to noisy data.