Technical drawings. Projection methods

2015 ◽  
Keyword(s):  
2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.


1988 ◽  
Vol 03 (13) ◽  
pp. 1285-1290 ◽  
Author(s):  
GÉRARD CLEMENT ◽  
JACQUELINE STERN

The generation of physical states from mean field hedgehogs by cranking is extended to coherent hedgehogs, thus improving the agreement between the cranking and coherent state projection methods, and enabling us to correct simultaneously for translational and rotational fluctuations. These corrections lead to a drastic reduction in the mean nucleon-delta mass which, for the physical values of mπ and Fπ, is lower than, or approximately equal to, the experimental value.


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