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Principal Components Analysis (PCA) and neural networks
(See previous post: Quality control of mechanical pieces by artificial vision)
In one hand PCA is a useful statistical technique that has found application in fields such as face recognition, image compression or gas sensors, and is a common technique for finding patterns in data of high dimension. On the other hand, a neural network, in the case of artificial neurons called artificial neural network (ANN), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation.
The following MATLAB code corresponds to the field of face recognition. Face recognition system is based on principal component analysis and neural network.
[Download source code with GNU GPL licence: Download source code zip] Also available at GitHub: https://github.com/txemaromero/facerecognition
In one hand PCA is a useful statistical technique that has found application in fields such as face recognition, image compression or gas sensors, and is a common technique for finding patterns in data of high dimension. On the other hand, a neural network, in the case of artificial neurons called artificial neural network (ANN), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation.
The following MATLAB code corresponds to the field of face recognition. Face recognition system is based on principal component analysis and neural network.
[Download source code with GNU GPL licence: Download source code zip] Also available at GitHub: https://github.com/txemaromero/facerecognition
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