Sparse principal component analysis by choice of norm
نویسندگان
چکیده
منابع مشابه
Optimal sparse L1-norm principal-component analysis
We present an algorithm that computes exactly (optimally) the S-sparse (1≤S<D) maximum-L1-norm-projection principal component of a real-valued data matrix X ∈ RD×N that contains N samples of dimension D. For fixed sample support N , the optimal L1-sparse algorithm has linear complexity in data dimension, O (D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has ...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2013
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2012.07.004