Spectral regression based marginal Fisher analysis dimensionality reduction algorithm
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.05.097
Abstract: Abstract Traditional nonlinear dimensionality reduction methods, such as multiple kernel dimensionality reduction and nonlinear spectral regression (SR), are generally regarded as extended versions of linear discriminant analysis (LDA) in the supervised case. As is well… read more here.
Keywords: analysis; dimensionality reduction; marginal fisher; fisher analysis ... See more keywords
Marginal Fisher Analysis With Polynomial Matrix Function
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DOI: 10.1109/access.2022.3208901
Abstract: Marginal fisher analysis (MFA) is a dimensionality reduction method based on a graph embedding framework. In contrast to traditional linear discriminant analysis (LDA), which requires the data to follow a Gaussian distribution, MFA is suitable… read more here.
Keywords: matrix function; mfa; fisher analysis; analysis ... See more keywords