Data representation in a stacked denoising autoencoder is investigated. Decoding is a simple technique for translating a stacked denoising autoencoder into a composition of denoising autoencoders in the ground space. In the infinitesimal limit, a composition of denoising autoencoders is reduced to a continuous denoising autoencoder, which is rich in analytic properties and geometric interpretat...

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised ma...

In this work we propose an lp-norm data fidelity constraint for training the autoencoder. Usually the Euclidean distance is used for this purpose; we generalize the l2-norm to the lp-norm; smaller values of p make the problem robust to outliers. The ensuing optimization problem is solved using the Augmented Lagrangian approach. The proposed lp -norm Autoencoder has been tested on benchmark deep...

As one of the most popular unsupervised learning approaches, the autoencoder aims at transforming the inputs to the outputs with the least discrepancy. The conventional autoencoder and most of its variants only consider the one-to-one reconstruction, which ignores the intrinsic structure of the data and may lead to overfitting. In order to preserve the latent geometric information in the data, ...

Single-layer stacked autoencoders have been shown to be successful in training artificial neurons with receptive fields that are similar to those found in the V1 cortex, but on monocular data. In this project we investigate extending a single-layer stacked autoencoder network to learn receptive fields on stereo data, and evaluate them with respect to their effectiveness as features for object c...

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised ...

This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited vari...

Electronic health records (EHRs) have contributed to the computerization of patient records so that they can be used not only for efficient and systematic medical services, but also for research on data science. In this paper, we compared the disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing value prediction...

Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its...