Kernel Principal Component Analysis Allowing Sparse Representation and Sample Selection
نویسندگان
چکیده
منابع مشابه
Sparse Kernel Principal Component Analysis
'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method, where a kernel function implicitly defines a nonlinear transformation into a feature space wherein standard PCA is performed. Unfortunately, the technique is not 'sparse', since the components thus obtained are expressed in terms of kernels associated with every trainin...
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ژورنال
عنوان ژورنال: ECTI Transactions on Computer and Information Technology (ECTI-CIT)
سال: 2019
ISSN: 2286-9131,2286-9131
DOI: 10.37936/ecti-cit.2019131.187506