Multisensor-Based Heavy Machine Faulty Identification Using Sparse Autoencoder-Based Feature Fusion and Deep Belief Network-Based Ensemble Learning

Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification

Deep neural network (DNN)-based approaches have been shown to be effective in many automatic speech recognition systems. However, few works have focused on DNNs for distant-talking speaker recognition. In this study, a bottleneck feature derived from a DNN and a cepstral domain denoising autoencoder (DAE)-based dereverberation are presented for distant-talking speaker identification, and a comb...

Ensemble Learning Based Sparse High-Order Boltzmann Machine for Unsupervised Feature Interaction Identification

Identifying interpretable high-order feature interactions is important specially for biomedical applications. In an unsupervised setting, it is a challenging structure learning task. In this paper, we propose an Ensemble Learning based Sparse High-order Boltzmann Machine (ELSHBM) to identify interpretable high-order feature interactions. By converting the problem of data log-likelihood maximiza...

Multisensor Images Fusion Based on Feature-Level

Until now, of highest relevance for remote sensing data processing and analysis have been techniques for pixel level image fusion. So, This paper attempts to undertake the study of Feature-Level based image fusion. For this purpose, feature based fusion techniques, which are usually based on empirical or heuristic rules, are employed. Hence, in this paper we consider feature extraction (FE) for...

[Article] EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder

Sparse Feature Learning for Deep Belief Networks

Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the raw input. Many unsupervised methods are based on reconstructing the input from the representation, while constraining the representation to have certain desirable properties (e.g. low dimension, sparsity, etc). Others a...