Search Results - Nonnegativity-constrained autoencoder

Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder by Chen, Jie, Wu, ZhongCheng, Zhang, Jun

“… identification accuracy and long prediction time. We first propose using an unsupervised three-layer nonnegativity-constrained autoencoder to adaptive search the optimal size of the sliding window, then construct a deep nonnegativity-constrained…”
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Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data by Chen, Jie, Wu, ZhongCheng, Zhang, Jun

“… In this paper, we propose a novel cost-sensitive L 1 /L 2 -nonnegativity-constrained deep autoencoder network for driving safety risk prediction…”
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Learning a referenceless stereopair quality engine with deep nonnegativity constrained sparse autoencoder by Jiang, Qiuping, Shao, Feng, Lin, Weisi, Jiang, Gangyi

“…) images based on deep nonnegativity constrained sparse autoencoder (DNCSAE). To address the quality issue of stereopairs whose perceived quality is not only…”
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Milling tool condition monitoring for difficult-to-cut materials based on NCAE and IGWO-SVM by Wang, Siqi, Yan, Shichao, Sun, Yuwen

“… To handle this issue, this paper proposed a novel method for monitoring tool wear conditions based on the nonnegativity-constrained autoencoder (NCAE…”
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Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data by Ayinde, Babajide O, Zurada, Jacek M

“… This is especially prominent when multilayer deep learning architectures are used. This paper demonstrates how to remove these bottlenecks within the architecture of Nonnegativity Constrained Autoencoder (NCSAE…”
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Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints by Hosseini-Asl, Ehsan, Zurada, Jacek M., Nasraoui, Olfa

“… (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data…”
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Cross-covariance regularized autoencoders for nonredundant sparse feature representation by Chen, Jie, Wu, ZhongCheng, Zhang, Jun, Li, Fang, Li, WenJing, Wu, ZiHeng

“… Existing feature representation algorithms based on the sparse autoencoder and nonnegativity-constrained autoencoder tend to produce duplicative encoding and decoding receptive fields, which leads…”
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Nonredundant sparse feature extraction using autoencoders with receptive fields clustering by Ayinde, Babajide O., Zurada, Jacek M.

“… Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading…”
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