ArticleJournal of multivariate analysis2019
Sparse quadratic classification rules via linear dimension reduction.
Article in Journal of multivariate analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications.Frontiers in oncology · 2023Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Identification of resistance in Escherichia coli and Klebsiella pneumoniae using excitation-emission matrix fluorescence spectroscopy and multivariate analysis.Scientific reports · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
We consider the problem of high-dimensional classification between two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we propose to perform simultaneous variable selection and linear dimension reduction on the original data, with the subsequent application of quadratic discriminant analysis on the reduced space. In contrast to quadratic discriminant analysis, the proposed framework doesn't require the estimation of precision matrices; it scales linearly with the number of measurements, making it especially attractive for the use on high-dimensional datasets. We support the methodology with theoretical guarantees on variable selection consistency, and empirical comparisons with competing approaches. We apply the method to gene expression data of breast cancer patients, and confirm the crucial importance of the ESR1 gene in differentiating estrogen receptor status.
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Registered trials
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