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February 3, 2026Journal of Multivariate Analysis

Ultrahigh-dimensional quadratic discriminant analysis using random projections

Annesha Deb Minerva Mukhopadhyay Subhajit Dutta

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Key Points

Effective classification is achieved with ultrahigh-dimensional data using quadratic discriminant analysis and random projections.
Key metric shows reduced computational complexity compared to traditional methods, effectively handling high-dimensional feature sets.
Assessment using quadratic discriminant analysis integrates random projections to enhance dimensionality reduction techniques.
This approach suggests potential improvements in data processing, though application limits in generalizability may exist.

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Implication

Analysis demonstrates effective classification of high-dimensional data using random projections, suggesting a new machine learning approach.

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Cite This Study

Deb et al. (2026) studied this question.

synapsesocial.com/papers/69a760b2c6e9836116a2db14 https://doi.org/10.1016/j.jmva.2026.105618

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