Exploring high-dimensional biological data with sparse contrastive principal component analysis
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: Bioinformatics
سال: 2020
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btaa176