Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Training products of experts by minimizing contrastive divergence
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
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Pavement distress images are typical high dimensional nonlinear data. Manifold learning algorithms can find the intrinsic characteristic hidden in the distress images, which helps to better recognize them. Unlike most of manifold learning algorithms, multilayer autoencoders have solved the data reconstructed problem through building a bi-directional mapping between the high dimensional data and the low dimensional data. An automatic pavement distress image recognition method based on multilayer autoencoders was proposed, which combined the image processing method and multilayer autoencoders. In the method, the distress images were firstly processed with the image processing method. Then the images were reduced dimensions and reconstructed with multilayer autoencoders. Lastly, the distress type was recognized through the network. Experiments showed that the recognition accuracy with the proposed method was great higher than that with the BP neural network.