Classification of sparse high-dimensional vectors
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Publication:3559954
DOI10.1098/rsta.2009.0156zbMath1185.62115OpenAlexW2131759077WikidataQ51787290 ScholiaQ51787290MaRDI QIDQ3559954
Alexandre B. Tsybakov, Christophe Pouet, Yuri I. Ingster
Publication date: 8 May 2010
Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1098/rsta.2009.0156
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian problems; characterization of Bayes procedures (62C10)
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Cites Work
- Higher criticism for detecting sparse heterogeneous mixtures.
- Goodness-of-fit tests via phi-divergences
- Estimation and confidence sets for sparse normal mixtures
- Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
- Feature selection by higher criticism thresholding achieves the optimal phase diagram
- Theoretical Measures of Relative Performance of Classifiers for High Dimensional Data with Small Sample Sizes
