Nonlinear Principal Component Analysis A simple and effective source code for Face Recognition Based on Nonlinear PCA.
Download

Nonlinear Principal Component Analysis Ranking & Summary

Nonlinear Principal Component Analysis Tags

face recognition nonlinear editor Nonlinear compressor nonlinear face recognition face based login nonlinear element

Nonlinear Principal Component Analysis Description

Dimensionality reduction greatly facilitates pattern classification. Various techniques, linear and nonlinear, have been widely proposed and used for dimensionality reduction in face recognition systems. Principle Component Analysis (PCA) has proved to be a simple and efficient linear method; while many nonlinear methods such as kernel PCA, have been proposed recently. Nonlinear principal component analysis (NLPCA) is commonly seen as a nonlinear generalization of standard principal component analysis (PCA). It generalizes the principal components from straight lines to curves (nonlinear). Thus, the subspace in the original data space which is described by all nonlinear components is also curved. Nonlinear PCA can be achieved by using a neural network with an autoassociative architecture also known as autoencoder, replicator network, bottleneck or sandglass type network. Such autoassociative neural network is a multi-layer perceptron that performs an identity mapping, meaning that the output of the network is required to be identical to the input. However, in the middle of the network is a layer that works as a bottleneck in which a reduction of the dimension of the data is enforced. This bottleneck-layer provides the desired component values (scores). We have developed a simple algorithm that uses this nonlinear dimensionality reduction for face recognition. This approach does not require the detection of any reference point and it can be used for real-time applications.

Nonlinear Principal Component Analysis Related Software