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Pattern Recognition, 2021
Abstract Similar to binary and multi-class classifiers, one-class classifiers have to face the difficulty of ’curse of dimensionality’ when they are applied to deal with high-dimensional samples. As an efficient dimensionality reduction method, sparse coding tries to learn a set of over-complete bases to represent the given samples.
Hong-Jie Xing, Ya-Jie Liu, Zi-Chuan He
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Abstract Similar to binary and multi-class classifiers, one-class classifiers have to face the difficulty of ’curse of dimensionality’ when they are applied to deal with high-dimensional samples. As an efficient dimensionality reduction method, sparse coding tries to learn a set of over-complete bases to represent the given samples.
Hong-Jie Xing, Ya-Jie Liu, Zi-Chuan He
openaire +1 more source
A penalty method for nonparametric estimation of the logarithmic derivative of a density function
Annals of the Institute of Statistical Mathematics, 1985This paper deals with the estimation of the function \(\psi_ 0=-f'\!_ 0/f_ 0\) from independent random variables \(X_ 1,...,X_ n\) with common unknown density \(f_ 0\) and cumulative distribution function \(F_ 0\). From the remark that, under mild conditions, \(\psi_ 0\) is the unique minimizer of \(\int [\psi^ 2-2\psi ']dF_ 0\) the author proposes as ...
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The albedo–climate penalty of hydropower reservoirs
Nature Energy, 2021Georg Wohlfahrt +2 more
exaly
Linking ghost penalty and aggregated unfitted methods
Computer Methods in Applied Mechanics and Engineering, 2022Santiago Badia +2 more
exaly
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry, 2000Carlos A Coello Coello
exaly