Super-sparse principal component analyses for high-throughput genomic data
نویسندگان
چکیده
منابع مشابه
Principal Component Analysis for Sparse High-Dimensional Data
Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solving PCA, but a number of other algorithms have been proposed. For instance, the EM algorithm is much more efficient in case of high dimensionality and a small number of principal components. We study a case where the data are hi...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2010
ISSN: 1471-2105
DOI: 10.1186/1471-2105-11-296