Edge-group sparse PCA for network-guided high dimensional data analysis
نویسندگان
چکیده
منابع مشابه
PCA learning for sparse high-dimensional data
– We study the performance of principal component analysis (PCA). In particular, we consider the problem of how many training pattern vectors are required to accurately represent the low-dimensional structure of the data. This problem is of particular relevance now that PCA is commonly applied to extremely high-dimensional (N 5000–30000) real data sets produced from molecular-biology research p...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2018
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bty362