Reducing the dimensionality of data with neural networksHakky St
(1) The document describes using neural networks called autoencoders to perform dimensionality reduction on data in a nonlinear way. Autoencoders use an encoder network to transform high-dimensional data into a low-dimensional code, and a decoder network to recover the data from the code.
(2) The autoencoders are trained to minimize the discrepancy between the original and reconstructed data. Experiments on image and face datasets showed autoencoders outperforming principal components analysis at reconstructing the original data from the low-dimensional code.
(3) Pretraining the autoencoder layers using restricted Boltzmann machines helps optimize the many weights in deep autoencoders and scale the approach to large datasets.
Reducing the dimensionality of data with neural networksHakky St
(1) The document describes using neural networks called autoencoders to perform dimensionality reduction on data in a nonlinear way. Autoencoders use an encoder network to transform high-dimensional data into a low-dimensional code, and a decoder network to recover the data from the code.
(2) The autoencoders are trained to minimize the discrepancy between the original and reconstructed data. Experiments on image and face datasets showed autoencoders outperforming principal components analysis at reconstructing the original data from the low-dimensional code.
(3) Pretraining the autoencoder layers using restricted Boltzmann machines helps optimize the many weights in deep autoencoders and scale the approach to large datasets.
