Overview
What is Nonlinear Principal Component Analysis?
Nonlinear Principal Component Analysis : A simple and effective source code for Face Recognition Based on Nonlinear PCA.
In depth
A closer look at Nonlinear Principal Component Analysis
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). Nonlinear Principal Component Analysis is 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.
Verdict
Should you download Nonlinear Principal Component Analysis?
Nonlinear Principal Component Analysis runs on
Windows 10/11
and is available under the
Freeware
license
— the installer is 565 KB.
We’ve catalogued it under
Science and Engineering.
✓
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At a glance
Nonlinear Principal Component Analysis specifications
- Publisher
-
Luigi Rosa
- Last updated
- Apr 30, 2026
- License
- Freeware
- Operating system
- Windows 10/11
- File size
- 565 KB
- Price
- Free
- Page views
- 554
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