Open AccessEpanechnikov kernel for incomplete data
Author(s) -
Mesquita D.P.P.,
Gomes J.P.P.,
Souza Junior A.H.
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.0507
Subject(s) - kernel (algebra) , kernel method , feature (linguistics) , kernel embedding of distributions , computer science , variable kernel density estimation , pattern recognition (psychology) , artificial intelligence , radial basis function kernel , function (biology) , mathematics , polynomial kernel , feature vector , value (mathematics) , algorithm , support vector machine , machine learning , discrete mathematics , evolutionary biology , biology , linguistics , philosophy
The Epanechnikov kernel (EK) is a popular kernel function that has achieved promising results in many machine learning applications. Although the EK is widely used, its basic formulation requires fully observed input feature vectors. A method is proposed to estimate the EK when these input vectors are only partially observed, i.e. some of its features are missing. In the proposed method, named expected EK, the expected value of the kernel function is estimated given the distribution of the data and the observed values of the feature vectors.